Analysis of Hydrogeochemical Processes Regulating Groundwater Quality in a Tropical Agricultural Landscape of Northwestern Mexico

Analysis of Hydrogeochemical Processes Regulating Groundwater Quality in a Tropical Agricultural Landscape of Northwestern Mexico | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Hydrogeochemical Processes Regulating Groundwater Quality in a Tropical Agricultural Landscape of Northwestern Mexico Yaneth A. Bustos-Terrones, Omar Mendoza-Aguilar, Juan G. Loaiza, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8629843/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract This study assesses the hydrochemical and water quality characteristics of the Mocorito River Aquifer (MRA), located in a tropical region with strong agricultural influence. Water data from seven sites of the aquifer, collected during 2012–2022, were used and evaluated through hydrogeochemical features, water quality index (WQI), and multivariate analysis to identify spatial and seasonal patterns, and anthropogenic effects. The results found low total nitrogen (9.7084 mg/L) but high mineralization and water hardness. High levels of sodium (up to 630.4 mg/L) and fecal coliforms (up to 24196 NMP/100mL) make the water unsuitable for both drinking and irrigation. Spatial and seasonal analyses showed heterogeneity among sites, with the greatest water quality deterioration in agricultural areas resulting from intensive fertilization and the leaching of soluble salts and fertilizer-derived ions (NO₃⁻, Cl⁻, SO₄²⁻, HCO₃⁻, Na⁺, Ca²⁺, Mg²⁺) into the aquifer due to excessive irrigation, and in urban areas from microbiological contamination. Hydrogeochemical assessment indicated that aquifer composition results from the interaction of natural processes (silicate and carbonate dissolution, ion exchange). Piper and USSL diagrams were used to characterize hydrochemical facies and evaluate irrigation suitability, while multivariate analysis demonstrated that groundwater quality in the MRA is controlled by the combined effects of geogenic processes, water–rock interactions, and anthropogenic influences. The MRA aquifer generally maintains good-to-moderate water quality, but localized zones show severe deterioration due to salinity and agricultural pollution, indicating the need for continuous monitoring and sustainable management. Water quality parameters Sustainable management Environmental indicators Groundwater quality Hydrochemistry Agricultural impact Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Groundwater is one of the main sources of water supply for human consumption, agricultural, and industrial activities worldwide (Khan et al. 2023 ). Its importance is accentuated in semiarid regions such northwestern Mexico (State of Sinaloa), where surface water sources are highly variable and subject to climatic stress and overexploitation (Bustos-Terrones et al. 2024). Groundwater quality depends on multiple factors, including natural hydrogeochemical processes, hydroclimatic conditions, land use changes, and anthropogenic activities that generate diffuse or point-source pollution (Wu et al. 2025 ). Particularly in densely populated urban areas, aquifer recharge zones are highly vulnerable to the infiltration of contaminants originating from landfills, untreated wastewater, and agricultural runoff, thereby compromising the water security of local communities (Krishnamoorthy & Lakshmanan, 2025 ). Seasonal spatial analysis of groundwater quality has emerged as a critical tool for monitoring and integrated resource management, allowing the identification of spatiotemporal patterns, risk zones, and dominant processes that affect the chemical composition of water (Xie et al. 2025 ). Recent studies, such as that of Pande et al ( 2025 ) in the Morna River Basin (India), have shown that groundwater quality varies significantly between pre- and post-monsoon seasons, influenced by processes such as water recharge, contaminant transport, and agricultural activity. Similarly, research conducted in industrial areas such as Ranipet (India) has detected seasonal fluctuations in the concentrations of heavy metals and dissolved salts, linked to anthropogenic sources and climatic dynamics (Wali et al. 2025 ). These findings highlight the need to adopt a seasonal geospatial approach to more accurately assess groundwater vulnerability, particularly in contexts of high agricultural and industrial pressure such as northwestern Mexico (Bahukhandi et al. 2025 ). Complementarily, hydrogeochemistry has proven to be fundamental for unraveling the physical, chemical, and biogeochemical processes that determine groundwater quality (Rivera-Hernández et al. 2021 ). The analysis of parameters such as pH, electrical conductivity, total dissolved solids, and the concentration of major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, SO₄²⁻, HCO₃⁻) allows for the characterization of water types and dominant processes, including mineral dissolution, weathering, ion exchange, and anthropogenic contamination (Salh et al. 2025 ; Hamma et al. 2024 ). In agricultural regions, where fertilizers and pesticides are widely applied, hydrogeochemistry also enables the detection of processes such as salinization, saline intrusion, or nitrate leaching, which pose potential risks to human health and ecosystems (Saeedi et al. 2024 ; Bahrami et al. 2022 ). The integration of multivariate statistical and geochemical modeling tools, such as principal component analysis (PCA), and hydrogeochemical zoning through clustering, has enhanced the geospatial analysis of aquifers, allowing for the distinction between natural and anthropogenic sources of contamination (Xie et al. 2025 ). Another key component in the comprehensive analysis of groundwater quality is the application of Water Quality Index (WQI), which synthesize multiple physicochemical parameters into a single scale that allows water to be classified according to its suitability for human or agricultural use (Bahrami et al. 2023). International studies have demonstrated that these indices, combined with GIS and statistical analyses, are effective tools for assessing water resource vulnerability, identifying critical zones, and supporting decision-making in public policy (Xie et al. 2025 ; Wu et al. 2025 ). For instance, in agricultural and industrialized areas of India, Algeria, and China, WQIs have revealed that between 20% and 60% of the groundwater analyzed does not meet the standards for human consumption due to the presence of nitrates, fluoride, and heavy metals (Mohammed et al. 2025 ; Kacha et al. 2025 ). Recent studies in southern Iran’s semi-arid regions have assessed groundwater quality for drinking, irrigation, and industrial uses. Bahrami et al ( 2022 ) highlighted spatial variability in hydrochemical parameters, while Bahrami et al ( 2024 ) applied WQI and GIS to identify areas at risk for potability. Bahrami and Zarei (2026) further demonstrated the utility of WQI combined with GIS and modeling as tools for integrated groundwater management. Overall, the geospatial analysis of groundwater quality, supported by hydrogeochemical, geochemical, and socio-environmental approaches, has been consolidated as a comprehensive methodology to evaluate the sustainability of groundwater resources (Bustos-Terrones et al. 2024). In the case of northwestern Mexico (Sinaloa), where agricultural pressure, urban growth, and climate variability are critical factors, it is essential to implement methodologies that integrate the spatial, temporal, and social dimensions of water quality. This study addresses the following research questions: (1) How do groundwater quality parameters in the MRA vary spatially and seasonally? (2) To what extent do agricultural practices influence these variations? (3) Which hydrogeochemical factors most significantly affect aquifer vulnerability? By answering these questions using hydrochemical variables and GIS tools, the study aims to provide scientific evidence for informed decision-making and sustainable water resource management. 2. Data and methodology 2.1 Data Analysis This study included seven sampling sites, each sampled twice per year from 2012 to 2022. The aquifer covers over a broad area of the Sierra region, while the selected sites are in zones affected by anthropogenic contamination from urban and agricultural activities (Rivera-Hernández et al. 2021 ). Sampling points were strategically positioned in the lower basin to effectively capture the main sources of pollution. The data used in this study were obtained from the National Water Quality Monitoring Network (RENAMECA)( https://www.gob.mx/conagua/es/articulos/resultados-de-la-red-nacional-de-medicion-de-calidad-del-agua-renameca ). As all data are publicly accessible through RENAMECA, no special permits or additional authorizations were required for this study. 2. 2 Groundwater Quality Assessment The evaluation of the physicochemical quality of groundwater from the Mocorito River Aquifer was conducted through the collection and analysis of samples from seven wells within the study area. The seven sampling sites were strategically selected in areas with significant anthropogenic activities, located in low-elevation zones of the aquifer surface. Because the Mocorito River Aquifer is a coastal aquifer, the monitoring wells have variable depths ranging from 2 to 12 m. Site 1 is downstream of the Eustaquio Buelna Dam within an agricultural area. Site 2 is near agricultural fields and close to a small village, while Site 3 lies within a region of intensive farming. Site 4 is situated very close to a small town, representing a semi-urban environment. Site 5 is positioned between agricultural lands and near two small ranches, and Site 6 is found among intensive agricultural zones near a small ranch. Finally, Site 7 is in the center of the city of Angostura, representing an urban setting. Collectively, these sites provide a representative overview of the aquifer’s water quality across different land uses, allowing an assessment of both natural and anthropogenic influences. Groundwater quality monitoring was carried out by collecting and analyzing water samples from wells chosen within the study region. The region has two well-defined climatic periods. The rainy season extends from June to October, while the remaining months correspond to the dry season, characterized by lower water availability and drier conditions. Sampling was performed during both the dry and rainy seasons to capture the temporal variation of the parameters. 2.3 Temporal and Spatial Analysis For the spatial analysis, data from seven sampling points located in the MRA were evaluated. Regarding the temporal analysis, the behavior of the water quality parameters was examined for the 2012–2022 period, considering seasonal variations and potential changes associated with natural or anthropogenic factors. In this analysis, the results were illustrated using interpolated maps with color coding. The maps were created using open-source software (QGIS 3.14). Each map employs a color gradient, where green tones represent low concentrations and red tones indicate high concentrations. 2.4 Hydrogeochemical Analysis and Data Clustering To identify the processes controlling the chemical composition of water, ionic ratio diagrams, TDS vs. TH, and NO₃⁻/Cl⁻ vs. Cl⁻ diagrams were constructed, as well as Piper diagrams, allowing differentiation of hydrochemical facies and evaluation of the influence of natural processes such as silicate and carbonate dissolution, weathering, and ion exchange, versus anthropogenic pressures associated with agriculture and wastewater. Irrigation suitability was assessed using the USSL diagram. 2. 5 Water Quality Index The WQI has been established as an effective tool for synthesizing complex water quality information into a single value reflecting its suitability for various uses (Basharat et al. 2025 ). Water classification was performed according to Equations 1 – 4 (Tajwar et al. 2025 ; Chaudhary et al. 2024 ). The parameters considered include pH, total dissolved solids, total hardness, calcium, magnesium, nitrates, chlorides, sulfates, fluoride, and total alkalinity. Water quality was assessed by considering the relative importance of each parameter. The evaluated parameters included pH (8.5, weight 4), total dissolved solids (500 mg/L, weight 4), total hardness (300 mg/L, weight 3), calcium (75 mg/L, weight 3), magnesium (30 mg/L, weight 3), nitrates (45 mg/L, weight 4), chlorides (250 mg/L, weight 2), sulfates (200 mg/L, weight 2), fluorides (1 mg/L, weight 4), and total alkalinity (200 mg/L, weight 2), with their respective guideline limits established by the WHO and distinct weights assigned according to their influence on overall water quality. $$\:{RW}_{i}=\frac{{w}_{i}}{{\sum\:}_{i}^{n}{w}_{i}}$$ 1 $$\:{q}_{i}=\frac{{e}_{i}-{v}_{i}}{{b}_{i}-{v}_{i}}\text{*}100$$ 2 $$\:{S.I.}_{i}={q}_{i\:}\text{*}{RW}_{i}$$ 3 $$\:WQI=\sum\:_{i=1}^{n}\left({S.I.}_{i}\right)$$ 4 where Wi indicates its significance and wi represents the weight assigned to each variable. The score of each parameter ( qi ) was calculated based on its measured concentration ( ei ) relative to the ideal value in pure water ( vi ), which is 0 for all parameters except pH, and the standard set by the WHO ( bi ). The WQI values are grouped into five categories: excellent water ( 300) (Bustos-Terrones et al. 2024). 2.6 Multivariate Statistical Analysis A multivariate statistical analysis was conducted to identify the processes controlling groundwater quality and to classify the sampling sites based on their hydrochemical similarity. Techniques such as Pearson correlation, principal component analysis, and hierarchical cluster analysis (HCA) were applied. Pearson correlation allowed the detection of linear associations between physicochemical and microbiological parameters, facilitating the identification of co-occurrence patterns and possible common contamination sources (Dheeraj et al. 2025 ; Chai et al. 2020 ). PCA reduced the dimensionality of the data and explained most of the variance through a limited number of components, highlighting dominant variables and groupings that reflect natural hydrochemical processes or anthropogenic influences (Kacha et al. 2025 ; Bahukhandi et al. 2025 ). Additionally, factor analysis with varimax rotation enabled the extraction of interpretable factors representing different contamination sources or specific hydrogeological conditions. Finally, HCA, using Ward’s method and Euclidean distance, grouped the sampling points according to physicochemical and microbiological similarities, delineating homogeneous zones, identifying spatial structures within the aquifer, and facilitating differentiated management of the groundwater resource (Alshahrani et al. 2025 ; Abdullahi et al. 2025 ). 3. Study Area In Mexico, there are 653 officially recognized aquifers. A considerable number of these exhibit some degree of overexploitation or lack of availability, which represents a significant challenge for the sustainable management of groundwater. The Mocorito River Aquifer is one of them, experiencing issues such as overexploitation, declining piezometric levels, and deterioration of water quality (Rivera-Hernández et al. 2017; Rivera-Hernández et al. 2021 ). The study area was selected due to its strategic importance for agricultural and mining development, as well as the interaction between human activities and water resources. The selection of the MRA is based on its regional economic importance and the challenges associated with sustainable groundwater management, including overextraction driven by agricultural demand, declining water quality linked to intensive fertilizer use and salinity accumulation, and increasing pressure from land-use change and prolonged drought conditions. The MRA exhibits topographic variation ranging from coastal plains to mountainous areas in the east, with the highest elevations, up to 565 m.a.s.l. located in the northeastern and southeastern extremes (Rivera-Hernández et al. 2017; Rivera-Hernández et al. 2021 ). The MRA, located northwest of Mexico, is a predominantly unconfined system composed of alluvial and fluvial sediments (including gravels, sands, silts, and clays) reaching thicknesses of over 200 m in the plain. The water table varies according to topography and recharge, generally ranging from 2 to 12 m below the surface, being shallower in the coastal plain and deeper toward the foothills. Groundwater flow is oriented approximately from northeast to southwest, parallel to the course of the Mocorito River. The aquifer is currently overexploited, with extraction exceeding recharge, and its proximity to the surface makes it relatively vulnerable to contamination. Nevertheless, its substantial thickness provides considerable groundwater storage, while its lithological composition influences geochemical processes that affect water quality. The seven selected monitoring sites provide a representative overview of the aquifer’s water quality (Table S1 presents the coordinates of the sampling points.). The aquifer extends from coastal to mountainous zones, with the central area characterized by rural settlements and intensive agriculture. The sites were strategically located in areas of highest anthropogenic impact, capturing the main factors affecting groundwater chemistry. As all sites belong to the same hydrogeological unit, the dataset reliably reflects the aquifer’s overall behavior, making the use of seven well-distributed sites methodologically sound. 4. Results and discussion 4.1 Water Quality Parameters The results were compared with the WHO guidelines and recent research reports (Table 1 ). Ammonia (0.0406 mg/L), nitrite (0.0133 mg/L), and nitrate (9.63 mg/L) were below the permissible limits, indicating low nitrogenous contamination and a moderate influence of agricultural practices or diffuse discharges, although nitrate is higher than reported in other studies (Saeedi et al. 2024 ; Kashif et al. 2025 ; Khan et al. 2023 ), suggesting a moderate influence of agricultural practices or diffuse discharges (Table S2 provides an overall statistical summary of the parameters, Table S3 shows ANOVA results comparing the seven sampling points, and Table S4 presents ANOVA results for the two seasonal periods, indicating that most parameters remained stable except for water temperature, which varied significantly between rainy and dry seasons). Table 1 Comparison of Groundwater Quality Parameters with WHO Guidelines and Literature Data. Parameter Abbreviation Unit Regulations* This study** Other studies Reference Ammonia NH₃ mg/L 0.5–1.5 0.0406 0.056 Saeedi et al. 2024 Nitrite NO₂ mg/L 3 0.0133 0.01 Khan et al. 2023 0.01 Mohamed et al. 2019 0.06 Saeedi et al. 2024 Nitrate NO₃ mg/L 50 9.6364 4.8 Kashif et al. 2025 36 Khan et al. 2023 14.8 Taloor et al. 2025 Phosphate PO₄ mg/L No guideline 0.3044 0.39 Sodomon et al. 2025 0.73 Saeedi et al. 2024 Total chlorides TCl mg/L 250 25.993 89.7 Khan et al. 2023 24.1 Taloor et al. 2025 9.48 Zakariah et al. 2025 Total dissolved solids TDS mg/L 600 1007.98 449.4 Kashif et al. 2025 573 Khan et al. 2023 876.3 Taloor et al. 2025 Hydrogen potential pH pH units 6.5–8.5 7.6063 7.3 Sodomon et al. 2025 6.9 Mohamed et al. 2019 7.47 Zakariah et al. 2025 Total fluorides TF mg/L 1.5 0.3344 0.8 Kashif et al. 2025 0.18 Khan et al. 2023 Silica SiO₂ mg/L No guideline 55.197 49.6 Sodomon et al. 2025 24.1 Zakariah et al. 2025 Electrical conductivity EC mS/cm² No guideline 1606.35 894.2 Kashif et al. 2025 1014 Khan et al. 2023 481 Taloor et al. 2025 Total hardness TH mg/L 200 488.164 294 Kashif et al. 2025 216.9 Taloor et al. 2025 258 Saeedi et al. 2024 Total alkalinity TA mg/L No guideline 379.242 235.4 Kashif et al. 2025 184 Saeedi et al. 2024 Sulfates SO₄ mg/L 250 165.576 48 Kashif et al. 2025 245.4 Khan et al. 2023 57.5 Taloor et al. 2025 Calcium Ca mg/L 75–200 126.978 103.7 Kashif et al. 2025 60 Taloor et al. 2025 16.3 Zakariah et al. 2025 Magnesium Mg mg/L 300 46.252 198.2 Kashif et al. 2025 22.1 Taloor et al. 2025 3.93 Zakariah et al. 2025 Potassium K mg/L 12 4.2349 2.5 Kashif et al. 2025 5.0 Khan et al. 2023 5.6 Taloor et al. 2025 Sodium Na mg/L 200 181.645 26.8 Kashif et al. 2025 31.4 Taloor et al. 2025 11.0 Zakariah et al. 2025 Bicarbonate HCO₃ mg/L 500 379.758 85.9 Sodomon et al. 2025 85.5 Zakariah et al. 2025 307.1 Mohamed et al. 2019 Water temperature WT °C No guideline 28.194 28.7 Kashif et al. 2025 27.7 Sodomon et al. 2025 *WHO Guidelines (2004). ** Average The groundwater chemistry indicated high salinity and hardness. The total dissolved solids (1008.0 mg/L) and electrical conductivity (1606.4 µS/cm) exceeded recommended limits and were higher than values reported in other comparable studies (Khan et al. 2023 ; Taloor et al. 2025 ). This mineralization was characterized by excessive total hardness (488.2 mg/L), suggesting a prevalence of calcium and magnesium salts, like the findings reported by Kashif et al ( 2025 ) and Saeedi et al ( 2024 ). Chlorides, sulfates, and total fluorides remained below the limits. The presence of silica (55.197 mg/L) and bicarbonates (379.758 mg/L) have no WHO guideline limits. Silica levels were higher than those reported in Zakariah et al ( 2025 ), which could be related to the weathering of silicate rocks. Total alkalinity (379.242 mg/L) indicated high buffering capacity, consistent with the findings of Mohamed et al ( 2019 ). Regarding cations, calcium (126.97 mg/L) and magnesium (46.25 mg/L) remained within the guidelines limits, although the values are high and contribute to water hardness. Sodium concentration (181.65 mg/L) was found close the guideline limit (200 mg/L), which could pose a risk for drinking water and irrigation. Potassium (4.23 mg/L) was within acceptable limits. Figure 2 presents boxplots showing that water quality tends toward high mineralization, evidenced by elevated total dissolved solids (TDS), electrical conductivity (EC), and total hardness (TH) (Al Haj et al. 2025 ; Sodomon et al. 2025 ; Xie et al. 2025 ). In several cases, these parameters exceed WHO (2017) limits, reaching TDS concentrations up to 3000 mg/L and hardness values near 1500 mg/L. This behavior indicates a predominance of dissolved salts and calcium and magnesium carbonates, consistent with previous studies in agricultural areas where mineralization is the main water quality issue (Kashif et al. 2025 ; Taloor et al. 2025 ). Although total alkalinity (TA) and bicarbonates (HCO₃⁻) enhance the buffering capacity of the water, they also contribute to hardness and reduce its suitability for human consumption and irrigation. Concerning nutrients, nitrate (NO₃⁻) and total nitrogen (TN) values remained within guidelines limits, with medians around 10–15 mg/L, reflecting moderate agricultural influence. However, outliers reaching up to 30 mg/L were identified, corresponding to localized fertilization inputs and leaching processes (Khan et al. 2023 ; Taloor et al. 2025 ). Ammonium (NH₃) and total phosphorus (TP) were found at low concentrations, indicating minimal recent organic pollution and low eutrophication risk, although some phosphate outliers suggest possible fertilizer discharge events (Sodomon et al. 2025 ). Basic water quality parameters indicate chemically stable conditions, with pH values within the acceptable range (7.0–8.2), suitable for aquatic life and human consumption after treatment. Silica (SiO₂) reaches relatively high values (40–70 mg/L), associated with silicate rock weathering and posing no regulatory risk, although it contributes significantly to mineralization (Zakariah et al. 2025 ). Regarding chlorides (TCl) and sulfates (SO₄²⁻), most records remain below guideline values, although peaks approaching 250 mg/L were observed, linked to anthropogenic discharges in specific areas (Khan et al. 2023 ). Overall, these results suggest that the main limitation to water quality in the area is not nutrient enrichment but salinization, hardness, and sporadic fecal contamination, which is consistent with observations reported in other agricultural regions (Saeedi et al. 2024 ; Kashif et al. 2025 ). 4.2 Spatial Analysis All sampling points were selected at strategic locations. The seven points in the aquifer are situated in areas with the highest anthropogenic activity, and thus are considered representative for adequately describing the current state of the aquifer. The spatial analysis of water quality in the MRA revealed similar behavior among the seven sampling points (SP) during the period 2012–2022, reflecting the interaction of natural processes and anthropogenic pressures (Fig. 3 ). At SP1, located near the Eustaquio Buelna reservoir, the highest concentrations of TOC (2.227 mg/L), nutrients (NO₂⁻, ON, TN, TP), total hardness (1090.36 mg/L), and electrical conductivity (3625 µs/cm 2 ) were observed, highlighting the influence of intensive fertilization practices and salt mobilization. At SP2, also agricultural but close to a human settlement, intermediate levels of nitrates (14.7 mg/L) and TDS (812.7 mg/L) were observed, reflecting the combined influence of agricultural practices and domestic discharges. SP3, located in an intensive farming area, exhibited high levels of fecal coliforms (211 MPN/100 mL), suggesting microbiological contamination linked to manure and wastewater use; the most critical microbial load was found at SP6, with 3474 MPN/100 mL of coliforms accompanied by phosphorus and ammonium, confirming severe water quality deterioration. Recent organic pollution at site SP5 was evidenced by high ammonium and fecal coliforms (138 MPN/100 mL), despite low nitrate concentrations (1.7 mg/L). This is consistent with the site's location in an area influenced by ranching and agriculture. Urban points reflected different scenarios: salt and nutrient concentrations were lower than at agricultural sites, but coliforms (88.5 MPN/100 mL) were detected at SP4, which are associated with domestic discharges. The city of Angostura (SP7) showed the lowest nutrient and salt levels, but the presence of coliforms (25.5 MPN/100 mL) indicates a moderate microbiological impact. These results confirm that agricultural sites (SP1, SP2, SP3, and SP6) concentrate the main problems of salinity, hardness, and fertilizer-derived nutrients, while urban sites (SP4 and SP7) present a higher sanitary risk due to microbiological contamination. These findings are consistent with the report by Rivera-Hernández et al (2017), who indicated weathering and evaporation as dominant processes in the aquifer’s chemical composition, but emphasized that groundwater is used for human consumption. According to these results, sustainable agricultural practices, control urban discharges, and continuous monitoring of the aquifer is required to ensure the quality and sustainability of the water resource in the region (Kashif et al. 2025 ). Finally, the analysis of microbiological parameters reveals that although most samples exhibit low fecal coliform (FC) concentrations, extreme values (24196 MPN/100mL, SP6, 2020) exceeding 10,000 MPN/100 mL indicate episodic contamination from domestic or livestock wastewater. 4.3 Seasonal Analysis The concentrations of water parameters from the seven sampling points of the MRA during the 2012–2022 period were analyzed. Figure 4 presents the results of the seasonal evaluation, which show marked fluctuations in water quality parameters, with peaks and declines in specific years. Overall, anthropogenic contaminants, such as nutrients and coliforms, tend to increase during the rainy seasons, associated with the runoff of fertilizers, organic matter, and wastewater into water bodies. In contrast, during the dry season, concentrations generally decrease due to reduced external inputs, although in some cases, evaporation concentrates dissolved salts, increasing parameters such as TDS, electrical conductivity (EC), and total hardness (TH). The nutrient dynamics (NH₃, NO₂⁻, NO₃⁻, TN, PO₄³⁻, TP) exhibit seasonal variations consistent with agricultural leaching and surface runoff processes. For example, 2014 data show a peak in nitrates (NO₃⁻: 14.26 mg/L) and total phosphorus (TP: 0.285 mg/L), coinciding with years of higher precipitation (2013 and 2014) and reflecting the relationship between agricultural practices and eutrophication risk. During dry years, such as 2021 and 2022, although external inputs decrease, some salt concentration is observed (TDS between 408 and 456 mg/L; EC between 1413 and 1514 µS/cm), reflecting seasonal mineralization, a natural process in which reduced water volumes and evaporation temporarily increase dissolved salts and nutrient concentrations, affecting water quality and salinity. In 2021 and 2022, reduced water inputs led to a prolonged dry season. As a result, an decremento in salt concentration was observed, with TDS ranging from 408 to 456 mg/L and EC from 1413 to 1514 µS/cm, reflecting a seasonal mineralization effect. This indicates that water quality is modulated by the interaction between natural processes (evaporation, hydrological cycles, mineralization) and human activities (agriculture, domestic discharges). The interannual variability of other parameters, such as bicarbonate (HCO₃⁻: 339–413 mg/L), calcium (Ca 2+ : 77–178 mg/L), and magnesium (Mg 2+ : 24–59 mg/L), suggests a stable hydrochemical behavior, with slight increases during dry seasons (november to may). TOC and chlorides exhibited fluctuations associated with organic and urban inputs. Seasonal peaks of nutrients and coliforms during rainy (june to october) periods highlight the risk of water quality deterioration and the potential impact on public health and aquatic ecosystems. A similar spatial and temporal dynamic is observed in other semi-arid basins. Pande et al ( 2025 ) reported significant variations between pre- and post-monsoon seasons, with marked differences in nutrients, salts, and coliforms according to land use. Krishnamoorthy and Lakshmanan ( 2025 ) demonstrated the influence of agricultural areas on fecal coliform and nutrient concentrations in India, while Wali et al ( 2025 ) reported comparable distribution patterns of phosphorus, nitrates, and salts, dependent on seasonal and geographic factors. 4.4 Hydrogeochemical species The hydrogeochemical analysis of the aquifer, based on ionic ratio diagrams (Fig. 5 ), indicates that natural and anthropogenic processes interact to determine the chemical composition of groundwater. In the Mg 2+ /Na + vs. Ca 2+ /Na + diagram (Fig. 5 a), the samples were in the silicate zone, suggesting that the chemistry is mainly controlled by the dissolution of silicate minerals (Al Haj et al. 2025 ; Xie et al. 2025 ). The HCO₃ 2 ⁻/Na + vs. Ca 2+ /Na + diagram (Fig. 5 b) reflected a Ca 2+ contribution dominated by carbonates, evidencing the dissolution of carbonate minerals (Dong et al. 2025 ). Figure 5 c (Ca 2+ /SO₄ 2 ⁻ vs. Ca 2+ ) showed that calcium mainly originates from the dissolution of carbonates/dolomites, while the contribution of sulfates is minor (Pande et al. 2025 ; Salh et al. 2025 ).The SO₄ 2 ⁻/Ca 2+ vs. NO₃⁻/Ca 2+ (Fig. 5 d) and NO₃⁻/Na + vs. Cl⁻/Na + (Fig. 5 e) diagrams evidenced the influence of agricultural activities and domestic wastewater, with significant contributions of nitrates and chlorides, combined with the influence of natural minerals (Zakariah et al. 2025 ; Sakthi Priya et al. 2025 ). Finally, the SO₄ 2 ⁻ + HCO₃ 2 ⁻ vs. Ca 2+ + Mg 2+ diagram Fig. 5 f indicated a balance between silicate weathering and calcite dissolution, reflecting an ionic balance close to electrochemical equilibrium and the stable interaction between natural mineral dissolution and silicate rock weathering processes (Teklearegay et al. 2025 ; Marouf et al. 2025 ; Miranda et al. 2025 ) (See Table S5). These results indicate that the chemical composition of the aquifer is the result of the interaction between natural geochemical processes and anthropogenic pressures, with a relatively homogeneous pattern in the sampled sites. The Piper diagram (Fig. 6 a) shows distinct hydrochemical facies, with SP6–SP7 dominated by Na²⁺–Cl⁻, SP2–SP4 by Ca²⁺–Mg²⁺–HCO₃⁻, and SP1, SP3, and SP5 exhibiting mixed characteristics. These patterns reflect the combined influence of natural geochemical processes and anthropogenic pressures. Ionic balance results indicate stable conditions at Sites 1 and 6, higher variability at Sites 2 and 5, and consistently low but stable values at Sites 4 and 7, supporting data reliability and site-specific groundwater quality differences. Figure 6 (b) shows the diagram of the United States Salinity Laboratory Staff (USSL), which classifies water quality into 16 zones (C1 to C4 and S1 to S4) to assess its suitability for irrigation (Tajwar et al. 2025 ). Zones C1 and S1 indicate minimal risk, while C4 and S4 represent very high risk for irrigation. According to this figure, the USSL diagram classifies water quality as excellent (C1S1), good (C1S2, C2S1, and C2S2), poor (C1S3, C2S3, C3S1, C3S2, and C3S3), and very poor (C1S4, C2S4, C3S4, C4S1, C4S2, C4S3, and C4S4) for irrigation purposes (Mohallel et al. 2025 ). Most of the water in the region (67%) shows quality suitable for irrigation, with acceptable salinity levels (EC) and low sodium adsorption risk (SAR), reflecting the overall good performance of the agricultural water resource. The SAR–EC diagram indicates that the best water quality occurs in wells such as SP3 and SP5, where both salinity and sodium levels remain low, supporting safe irrigation use. Approximately 17% of the samples fall into the “Poor” category, suggesting potential limitations for salinity-sensitive crops and the need for specific agricultural management practices. The remaining 16% of the samples, corresponding to SP1, are classified as “Very Poor,” representing a high risk to soils due to elevated salinity and high exchangeable sodium. Figure 6 (c) shows a clear distinction between soft, moderately hard, hard, and very hard waters, as well as between fresh and brackish waters. The distribution of sampling points in fields such as Hard-Fresh water and Hard-Brackish water reflects the influence of carbonate mineral dissolution, salt leaching, and the hydrogeochemical evolution of the aquifer. In the Fig. 6 (d), contamination sources are identified. High NO₃⁻ concentrations are mainly associated with local agricultural activities and human settlements. The increase in Cl⁻ indicates domestic infiltrations and salinization processes. The dispersion of the samples evidences the interaction of natural processes with anthropogenic pressures that enhance contaminant loads (Hossain et al. 2024 ). Similar results were reported by Rivera-Hernández et al (2017) in the MRA, where weathering and evaporation were identified as dominant processes. These findings are related to cumulative impacts of nitrogen fertilization and discharges of excreta and wastewater (Sodomon et al. 2025 ). 4.5 Multivariate Statistical Analysis The multivariate analysis was used to identify significant relationships among physicochemical parameters, nutrients, and microbiological variables, and to evidence the interaction between natural processes and anthropogenic pressures (Kashif Alam et al. 2025; Sodomon et al. 2025 ). Pearson correlation showed strong associations among NO₃⁻, NO₂⁻, TN, TOC, TDS, EC, and cations such as Ca²⁺, Mg²⁺, Na⁺, as well as HCO₃⁻, suggesting that water quality is simultaneously influenced by agricultural and domestic inputs and natural processes such as carbonate and silicate dissolution (Fig. 7a) (Table S6). These findings are consistent with previous studies in agricultural aquifers, where PCA and correlation analyses effectively distinguished the contributions of natural and anthropogenic sources (Chai et al. 2020 ). Principal Component Analysis revealed that two components explained most of the water quality variability (49.1%) in the aquifer. Parameters such as TDS, EC, TH, SO₄²⁻, Ca²⁺, Mg²⁺, and Mn²⁺ were associated with mineral dissolution processes, silicate and carbonate weathering, as well as inputs from fertilizers and domestic discharges (Fig. 7b) (Table S7). Other components highlighted the variability of nutrients and organic compounds (PO₄³⁻, TP, TOC, MBAS, FC), reflecting diffuse and domestic pollution. These patterns are consistent with findings in arid and semi-arid regions, where PCA enabled the identification of hydrochemical facies and predominant processes controlling groundwater quality (Liu et al. 2025a ; 2025b ; c ). Hierarchical cluster analysis allowed grouping hydrogeochemical variables according to their similarity, identifying three main clusters (Fig. 7c). Cluster 1 included NO₃⁻, TN, TA, HCO₃⁻, Na⁺, Ca²⁺, Mg²⁺, SO₄²⁻, EC, TDS, TH, and Mn²⁺, primarily associated with natural hydrogeochemical processes and mineralization. Cluster 2 grouped ON, MBAS, RP, and TCl, representing variables related to moderate anthropogenic inputs, while Cluster 3 included WT, FC, NH₃, TP, PO₄³⁻, NO₂⁻, TOC, K⁺, pH, TF, and SiO₂, reflecting recent contamination and active chemical–biological processes. These results demonstrate that groundwater quality in the MRA is governed by the interaction of geogenic processes, water–rock interactions, and human contributions. These results emphasize the importance of multivariate methods for identifying critical parameters and planning sustainable monitoring and management strategies (Fallatah et al. 2023; Hagage et al. 2025 ; Laghrib et al. 2025 ). 4.6 Water Quality Index Figure 8 presents the spatial distribution of the WQI in the Northwest Mexico, with a focus on the MRA. In Sinaloa, the WQI ranges from 10 (best quality, in blue) to 200 (worst quality, in red). Green points correspond to water sampling stations or sites. Spatial interpolation shows that most sampling sites exhibit low water quality index values (blue and green tones), indicating good water quality, with only isolated areas showing higher values. A WQI gradient is observed, ranging from green to red, with a central core in red and yellow tones indicating the highest index values, associated with poorer water quality. Sampling points are distributed across both low and high index areas, allowing for the spatial identification of critical zones. The scatter plot between the WQI and Total Dissolved Solids (TDS) for the aquifers of Sinaloa shows a clear positive correlation: as the concentration of dissolved solids increases, the water quality index deteriorates. This reflects both natural salinization processes and the influence of intensive agricultural activities and wastewater discharges (Abiye et al. 2025; Pandey et al. 2023 ). 4.7 Contributions of this work to global climate change mitigation This study contributes to climate change mitigation through the sustainable management of water resources and pollution control. By enhancing the understanding of groundwater quality, it enables the design of strategies that reduce aquifer overexploitation and increase the resilience of ecosystems and communities under adverse climatic scenarios. The identification of pollution sources allows for the implementation of targeted measures that indirectly decrease greenhouse gas emissions by reducing energy-intensive water treatments and promoting sustainable agricultural practices. Furthermore, it highlights the importance of sustainable water and waste management policies, fostering the reduction of environmental degradation and improving carbon sequestration, thereby strengthening water security and contributing to global climate change mitigation. 5. Conclusions The groundwater quality of the MRA is characterized by low contamination from nitrogenous and phosphorous nutrients, but exhibits high mineralization, hardness, and dissolved salts concentration, which limits its suitability for human consumption and irrigation without treatment. Spatial and seasonal variability indicates that agricultural sites concentrate the highest salinity and nutrient issues, while urban areas show moderate microbiological risks. Hydrogeochemical analyses suggest that the aquifer’s composition results from the interaction between natural processes, such as silicate and carbonate dissolution, and anthropogenic pressures, including agricultural fertilization and domestic wastewater discharges. Multivariate analyses, Piper diagram, allowed the identification of contamination patterns, dominant hydrochemical facies, and critical parameters, highlighting the usefulness of these tools for sustainable resource management. Finally, the observed inverse relationship between water availability and the Water Quality Index indicates that overexploitation and intensive agricultural practices contribute to water quality degradation, emphasizing the need to implement differentiated management strategies that integrate recharge, salinity control, and reduction of pollutant inputs to ensure the aquifer’s sustainability. Future research Future research should focus on continuous monitoring of the MRA to capture temporal variations in water quality, particularly during rainy (june to october) and dry seasons (november to may), when nutrient leaching, salinization, and microbial contamination are more pronounced. Long-term datasets would allow for better understanding of seasonal dynamics and the impact of agricultural practices on groundwater. Hydrogeochemical modeling and spatial-temporal analysis are recommended to predict the evolution of water quality under different land use and climate change scenarios. Research should also explore mitigation measures to reduce salinity, hardness, and nutrient loads, including sustainable irrigation practices, artificial recharge, and targeted treatments for microbial contamination. These strategies can support both environmental protection and public health. Finally, integrating hydrochemical, land use, and socio-economic data can inform adaptive management policies, enabling sustainable groundwater use and preservation in tropical agricultural regions. Such interdisciplinary approaches are essential to ensure long-term water quality and availability. Declarations Ethical approval All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests The authors declare no competing interests. Funding No funding was received for this study. Author Contribution YABT: Project administration, Investigation, Writing – original draft. OMA: Investigation, Formal analysis. JGL: Investigation, Writing –review & editing, Validation, Software. JGRP: Writing –review & editing. BRP: Visualization. JEM: Validation, Data curation. TAK: Writing –review & editing. MNRV: Validation. Acknowledgments The lead author gratefully acknowledges the support provided by SECIHTI through the Programa de Investigadoras e Investigadores por México (Project No. 7026). Data availability No datasets were generated or analyzed during the current study References Abdullahi IM, Abubakar L, Saidu G, Yusuf H (2025) Hydrogeochemical characterization of groundwater using water quality index and multivariate statistical analysis in Binji town and environs, Sokoto Basin, northwestern Nigeria. Appl Water Sci 15(2):33. https://doi.org/10.1007/s13201-025-02358-9 Abiye T, Raimi MO (2025) Assessing groundwater contamination near dumpsites in Port Harcourt using water quality index (WQI): insights from seasonal and distance-based variations. Int J Hydrology 9(1):35–44. https://doi.org/10.15406/ijh.2025.09.00401 Al Haj R, Merheb M, Halwani J, Ouddane B (2025) Baseline hydrogeochemical characteristics of groundwater in Abu Ali watershed (northern Lebanon). J Hydrology: Reg Stud 57:102135. https://doi.org/10.1016/j.ejrh.2024.102135 Alshahrani M, Ahmad M, Laiq M, Nabi M (2025) Geostatistical analysis and multivariate assessment of groundwater quality. Sci Rep 15(1):7435. https://doi.org/10.1038/s41598-025-91055-3 Bahrami A, Bahrami M, Haghani E (2024) Groundwater quality assessment for potable use using WQI and GIS technology in southern Iran. Sustainable Water Resour Manage 10(5):177. https://doi.org/10.1007/s40899-024-01155-7 Bahrami M, Khaksar E, Bahrami A (2022) Groundwater quality evaluation for potable and irrigation uses in the semi-arid region of southern Iran. Irrig Sci 71(3):749–765. https://doi.org/10.1002/ird.2671 Bahrami M, Zarei AR (2023) Assessment and modeling of groundwater quality for drinking, irrigation, and industrial purposes using water quality indices and GIS technique in Fasarud aquifer (Iran). Model Earth Syst Environ 9(4):3907–3921. https://doi.org/10.1007/s40808-023-01725-2 Bahukhandi KD, Sk B, Kamboj V, Bhan U, Goswami L, Kushwaha A et al (2025) Assessment of spring water hydrogeochemistry in the intermountain Doon Valley of the Himalayan region using water quality indexing and multivariate statistical methods. Water Air Soil Pollut 236(5):123. https://doi.org/10.1007/s11270-025-07892-5 Basharat H, Ahmed T, Ahmad SS, Zahir M, Scholz M (2025) Integrating water quality index and advanced geographic information system for groundwater quantity and quality mapping: insights from Islamabad’s aquifer. Sustainability 17(4):1373. https://doi.org/10.3390/su17041373 Bustos Terrones YA, Loaiza JG, Rojas-Valencia MN, Rangel-Peraza JG, Ramírez-Pereda B, García-Sánchez BE (2024) Hydrogeochemical characterization of groundwater located in an intensive agricultural area: the Culiacan River Aquifer case study. Water Resour 51(5):844–859. https://doi.org/10.1134/S0097807824603212 Chai Y, Xiao C, Li M, Liang X (2020) Hydrogeochemical characteristics and groundwater quality evaluation based on multivariate statistical analysis. Water 12(10):2792. https://doi.org/10.3390/w12102792 Chaudhary R, Gaur N, Yadav M (2024) Hydrogeochemical analysis of groundwater quality during the pre-monsoon season of Manipur, India. Water Sci 38(1):274–292. https://doi.org/10.1080/23570008.2024.2341369 Dheeraj VP, Singh CS, Alam A, Sonkar AK (2025) Hydrogeochemical quality investigation of groundwater resources using multivariate statistical methods, water quality indices, and health risk assessment in the Korba Coalfield region, India. Stoch Env Res Risk Assess 39:122. https://doi.org/10.1007/s00477-024-02895-w Dong F, Yin H, Yang Z, Zhou W, Cheng W, Liu Y (2025) Delineating the controlling mechanisms of geothermal water quality and suitability zoning in the Lower Yellow River Basin, China. Environ Technol Innov 38:104126. https://doi.org/10.1016/j.eti.2025.104126 Ebri E, Bassey NE, George NJ, Harry TA (2025) Geochemical analysis of groundwater in Central Cross River State: implications for water quality and public health. Researchers J Sci Technol 5(2):16–38 Fallatah O, Khattab MR (2023) Study of hydrogeochemical factors affecting groundwater quality used for land reclamation: application of multivariate statistical analysis. Stoch Env Res Risk Assess 37(12):4719–4735. https://doi.org/10.1007/s00477-023-02537-7 Hagage M, Hewaidy AGA, Abdulaziz AM (2025) Groundwater quality assessment for drinking, irrigation, aquaculture, and industrial uses in the waterlogged northeastern Nile Delta, Egypt: a multivariate statistical approach and water quality indices. Model Earth Syst Environ 11(1):59. https://doi.org/10.1007/s40808-024-02242-6 Hamma B, Alodah A, Bouaicha F, Bekkouche MF, Barkat A, Hussein EE (2024) Hydrochemical assessment of groundwater using multivariate statistical methods and water quality indices (WQIs). Appl Water Sci 14(2):33. https://doi.org/10.1007/s13201-023-02084-0 Hao Q, Xiao Y, Liu K, Yang H, Chen H, Wang L et al (2025) Spatial pattern of groundwater chemistry in a typical piedmont plain of Northern China driven by natural and anthropogenic forces. Sci Rep 15(1):7643. https://doi.org/10.1038/s41598-025-91659-9 Hossain MS, Nahar N, Shaibur MR, Bhuiyan MT, Siddique AB, Al Maruf A et al (2024) Hydrochemical characteristics and groundwater quality evaluation in south western region of Bangladesh: A GIS-based approach and multivariate analyses. Heliyon 10(1):e24011. https://doi.org/10.1016/j.heliyon.2024.e24011 Kacha N, Aouidane L, Boulabeiz M, Khammar H, Tellil B (2025) Integrated hydrochemical assessment of groundwater quality in El Mahmel Plain, Algeria: A hydrochemical, water quality index and multivariate statistical approach. Water Air Soil Pollut 236(7):121. https://doi.org/10.1007/s11270-025-08050-7 Kashif A, Muhammad N, Wajid A, Said M, Abdur R (2025) Geogenic contamination of groundwater in a highland watershed: Hydrogeochemical assessment, source apportionment, and health risk evaluation of fluoride and nitrate. Hydrology 12(4):70. https://doi.org/10.3390/hydrology12040070 Khan MYA, ElKashouty M, Abdellattif A, Egbueri JC, Taha AI, Al Deep M et al (2023) Influence of natural and anthropogenic factors on the hydrogeology and hydrogeochemistry of Wadi Itwad Aquifer, Saudi Arabia: Assessment using multivariate statistics and PMWIN simulation. Ecol Ind 151:110287. https://doi.org/10.1016/j.ecolind.2023.110287 Krishnamoorthy L, Lakshmanan VR (2025) Seasonal assessment of groundwater quality, hydrogeochemistry, and heavy metal pollution in groundwater at Ranipet District: employing multivariate statistics, agricultural indices, and health risk evaluation. EGU General Assembly 2025, EGU25-1041. https://doi.org/10.5194/egusphere-egu25-1041 Laghrib F, Elkasmi S, Bahaj T, Barbot A, Bouzekraoui M, Hilali M et al (2025) Integration of multivariate statistical analysis, geochemical modeling, and irrigation water quality assessment in the aquifers of the South Atlas Tinghir–Errachidia–Boudenib Basin (Pre-African Trough, Morocco). J Afr Earth Sc 221:105444. https://doi.org/10.1016/j.jafrearsci.2024.105444 Liu H, Hu X, Zhu H, Xing L, Han Z, Hu K et al (2025a) Analysis of the hydrogeochemical characteristics of groundwater and identification of pollution sources in facility agriculture areas using self-organizing neural networks. Environ Earth Sci 84(6):161. https://doi.org/10.1007/s12665-025-12114-6 Liu N, Chen M, Gao D, Wu Y, Wang X (2025b) Identification of hydrogeochemical processes in shallow groundwater using multivariate statistical analysis and inverse geochemical modeling. Environ Monit Assess 197(2):135. https://doi.org/10.1007/s10661-024-13528-8 Liu Y, Zhou L, Ma X, Li W, Li J (2025c) Comprehensive study of groundwater hydrochemistry, driving forces, and health risks in representative rural agglomerations, Northern China. ACS Omega 10(18):18391–18403. https://doi.org/10.1021/acsomega.4c10697 Marouf AA, Ameen HA, Qasim MJ (2025) Water quality index utilization for groundwater quality assessment for wells in Zakho District, Kurdistan Region, Iraq. Water Sci 39(1):325–335. https://doi.org/10.1080/23570008.2025.2496580 Miranda J, Antunes M, Ribeiro CA (2025) Groundwater modeling from urban areas (NW Portugal): An integrated hydrological–hydrogeological approach. Earth Syst Environ 9:1–18. https://doi.org/10.1007/s41748-025-00614-1 Mohallel SA, Morgan H, Elgendy A, Maharjan S, Fazli S, Li W et al (2025) Innovative machine learning, isotopic, and hydrogeochemical techniques for groundwater analysis in arid landscapes in Egypt’s Eastern Desert. Earth Syst Environ 9:1–25. https://doi.org/10.1007/s41748-025-00628-9 Mohamed AK, Liu D, Song K, Mohamed MA, Aldaw E, Elubid BA (2019) Hydrochemical analysis and fuzzy logic method for evaluation of groundwater quality in the North Chengdu Plain, China. Int J Environ Res Public Health 16(3):302. https://doi.org/10.3390/ijerph16030302 Mohammed MA, Szabó NP, Mikita V, Szűcs P (2025) Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of the Debrecen area, Hungary: Integration of classical and unsupervised learning methods. Environ Sci Pollut Res 32(11):6884–6903. https://doi.org/10.1007/s11356-025-36175-z Pande CB, Tolche AD, Egbueri JC, Mohd Sidek L, Singh R, Mishra AP et al (2025) Implications of seasonal variations of hydrogeochemical analysis using GIS, WQI, and statistical analysis method for the semi-arid region. Appl Water Sci 15(4):80. https://doi.org/10.1007/s13201-025-02387-4 Pandey HK, Singh VK, Srivastava SK, Singh RP (2023) Groundwater quality assessment using PCA and water quality index (WQI) in a drought-prone area. Sustainable Water Resour Manage 9(6):197. https://doi.org/10.1007/s40899-023-00963-7 Rivera-Hernández JR, Green-Ruiz CR, Pelling-Salazar LE, Flegal AR (2021) Monitoring of As, Cd, Cr, and Pb in groundwater of Mexico’s agriculture Mocorito River Aquifer: Implications for risks to human health. Water Air Soil Pollut 232(7):291. https://doi.org/10.1007/s11270-021-05238-5 Rivera Hernández JR, Green Ruiz C, Pelling Salazar L, Trejo Alduenda A (2017) Hidroquímica del acuífero costero del Río Mocorito, Sinaloa, México: Evaluación de la calidad del agua para consumo humano y agricultura. Hidrobiológica 27(1):103–113. https://doi.org/10.24275/uam/izt/dcbi/hidro/2017v27n1/Green Saeedi R, Sadeghi S, Massoudinejad M, Oroskhan M, Mohagheghian A, Mohebbi M et al (2024) Assessing drinking water quality based on water quality indices, human health risk, and burden of disease attributable to heavy metals in rural communities of Yazd County, Iran (2015–2021). Heliyon 10(13):e33984. https://doi.org/10.1016/j.heliyon.2024.e33984 Sakthi Priya R, Antony Ravindran A, Richard Abishek S (2025) Spatial assessment of submarine groundwater discharge influence on aquifer water quality in the coastal region of Chettikulam to Kolachel, southern India: Using SMI and HFED techniques. Environ Geochem Health 47(4):112. https://doi.org/10.1007/s10653-025-02379-y Salh YHM, Su C, Iqbal J, Usman US, Yousif MH, Ismail O (2025) Hydrogeochemical processes regulating groundwater quality and its suitability for drinking purposes in the recent alluvial plain. Blue Nile Region Sudan Environ Geochem Health 47(4):1–23. https://doi.org/10.1007/s10653-025-02409-9 Sarker MMR, Hermans T, Van Camp M, Hossain D, Islam M, Ahmed N et al (2022) Identifying the major hydrogeochemical factors governing groundwater chemistry in the coastal aquifers of Southwest Bangladesh using statistical analysis. Hydrology 9(2):20. https://doi.org/10.3390/hydrology9020020 Singha SS, Singha S, Kumar P (2025) Groundwater quality assessment in Nalgonda District, Telangana, India: A comprehensive approach using self-organizing map. Discover Sustain 6(1):185. https://doi.org/10.1007/s43621-025-00985-5 Sodomon AK, Akpataku KV, Tampo L, Mande SLAS, Herrera JB, Rosales WM et al (2025) Assessment of hydrogeochemical evolution of groundwater from the basement aquifer in the upper part of the transboundary Mono River Basin, Togo. J Hydrology: Reg Stud 58:102200. https://doi.org/10.1016/j.ejrh.2025.102200 Tajwar M, Rahman M, Hasan M, Sakib N, Shreya SS, Alam MMT et al (2025) Interpreting hydrogeochemical interactions and controlling processes in groundwater using advanced statistical techniques in the Southeast Asian megacity of Dhaka, Bangladesh. https://doi.org/10.1016/j.clwat.2025.100084 . Cleaner Water 100084 Tajwar M, Uddin A, Lee MK, Nelson J, Zahid A, Sakib N (2023) Hydrochemical characterization and quality assessment of groundwater in Hatiya Island, southeastern coastal region of Bangladesh. Water 15(5):905. https://doi.org/10.3390/w15050905 Taloor AK, Sambyal S, Sharma R, Dev S, Shastri S, Kumar R (2025) Advanced hydrogeochemical facies classification: A comparative analysis of machine learning models with SMOTE in the Tawi Basin. Phys Chem Earth Parts A/B/C 137:103785. https://doi.org/10.1016/j.pce.2024.103785 Teklearegay T, Atlabachew A, Abebe A, Jothimani M (2025) Comprehensive hydrogeochemical and statistical assessment of groundwater quality for drinking and irrigation in the Demie River catchment, Southern Ethiopia. Discover Appl Sci 7(5):1–35. https://doi.org/10.1007/s42452-025-06967-6 Wali SU, Alias NB, Usman AA, Umar A, Muhammad N, Kaoje IU et al (2025) Geostatistical and multivariate analysis of phosphate evolution and its relationship with heavy metals in shallow groundwater in a semi-arid basin. Earth Sci Inf 18(3):267. https://doi.org/10.1007/s12145-025-01771-7 Wu C, Zhou H, Lu C, Zhao Y, Liu R, Zhan L et al (2025) Groundwater nitrate responses to extreme rainfall in alluvial–diluvial plain aquifers: Evidence from hydrogeochemistry and isotopes. J Contam Hydrol 104584. https://doi.org/10.1016/j.jconhyd.2025.104584 Xie Z, Liu W, Chen S, Yao R, Yang C, Zhang X et al (2025) Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environments in a metropolis. J Hydrology: Reg Stud 58:102227. https://doi.org/10.1016/j.ejrh.2025.102227 Zakariah MNA, Roslan N, Sulaiman N, Aznan MA, Al Farishi B (2025) Hydrogeochemical assessment of groundwater quality of Muda River Basin, Kedah, Malaysia. Malaysian J Fundamental Appl Sci 21(1):1513–1528. https://doi.org/10.11113/mjfas.v21n1.3368 Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIALMRA.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 18 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8629843","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589892260,"identity":"31c182b0-fd96-44e8-976f-576c6f3957d4","order_by":0,"name":"Yaneth A. Bustos-Terrones","email":"","orcid":"","institution":"Instituto Tecnológico de Culiacán","correspondingAuthor":false,"prefix":"","firstName":"Yaneth","middleName":"A.","lastName":"Bustos-Terrones","suffix":""},{"id":589892261,"identity":"5223290c-e8ba-4704-9559-45c6f97a9ffc","order_by":1,"name":"Omar Mendoza-Aguilar","email":"","orcid":"","institution":"Instituto Tecnológico de Culiacán","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"","lastName":"Mendoza-Aguilar","suffix":""},{"id":589892262,"identity":"851c187a-576c-4ade-97b2-957208f5371b","order_by":2,"name":"Juan G. Loaiza","email":"","orcid":"","institution":"Instituto Tecnológico de Culiacán","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"G.","lastName":"Loaiza","suffix":""},{"id":589892263,"identity":"fd66c28f-14b8-4de4-913c-064626e06c84","order_by":3,"name":"Jesús Gabriel Rangel-Peraza","email":"","orcid":"","institution":"Instituto Tecnológico de Culiacán","correspondingAuthor":false,"prefix":"","firstName":"Jesús","middleName":"Gabriel","lastName":"Rangel-Peraza","suffix":""},{"id":589892264,"identity":"e1f186f0-524f-400a-a2b9-f75f97a5df80","order_by":4,"name":"Blenda Ramirez-Pereda","email":"","orcid":"","institution":"Instituto Tecnológico de Culiacán","correspondingAuthor":false,"prefix":"","firstName":"Blenda","middleName":"","lastName":"Ramirez-Pereda","suffix":""},{"id":589892265,"identity":"6768d76c-ce90-4fac-9499-b1b3f8404d9c","order_by":5,"name":"Tonni Agustiono Kurniawan","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Tonni","middleName":"Agustiono","lastName":"Kurniawan","suffix":""},{"id":589892266,"identity":"75ac0d28-6a15-4350-aabf-aee218a349bd","order_by":6,"name":"Jesús Estrada-Manjarrez","email":"","orcid":"","institution":"Instituto Tecnológico de Culiacán","correspondingAuthor":false,"prefix":"","firstName":"Jesús","middleName":"","lastName":"Estrada-Manjarrez","suffix":""},{"id":589892267,"identity":"8af28b61-d446-4a78-b95b-9289d5004a12","order_by":7,"name":"María Neftalí Rojas-Valencia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIie3QMYvCMBTA8ecV4tLenEX9Cq8EBKFwXyVFqIsFR0enTHrOfos4ebcVCi4WXOOk4OKgoNsdVLhEcLzYUTD/6Q35kZcAuFxPGG4zQIC8SbyqpKP4jbDq5EMTXR6PKi/WVt1s8FMmvc96kB8uXxAv6iv+thEWsl3ycCKSVHjvSWdWQPw9TqXXL2zERwxGhvhtFgiIZWbI8H+CmzGGZRn1iCFXQ9YnTdBCVIHMJxHXhO1rhqhHt6iEs4agoVmsNhGUSXWSue0tqLrL8FjSVmtasPOviBpync73qeXHdOS+N6EA9DZlVgDg7e7D+cFJl8vletH+AKKRVq7zkh0iAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Nacional Autónoma de México, Instituto de Ingeniería, CDMX","correspondingAuthor":true,"prefix":"","firstName":"María","middleName":"Neftalí","lastName":"Rojas-Valencia","suffix":""}],"badges":[],"createdAt":"2026-01-18 07:53:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8629843/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8629843/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102512618,"identity":"d78c28f0-212c-436f-8e11-faa6533631f4","added_by":"auto","created_at":"2026-02-12 12:56:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194851,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic Location, Topographic Elevation of the Mocorito River Aquifer and ubicación del sampling point 1 (SP1), sampling point 2 (SP2), sampling point 3 (SP3), sampling point 4 (SP4), sampling point 5 (SP5), sampling point 6 (SP6) and sampling point 7 (SP7). The base map is reproduced according to the Esri Terms of Use, which permit use in academic publications with attribution.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/227d5f194a799b8d40abb878.jpg"},{"id":102512617,"identity":"12b0d447-ba56-4676-b181-09432e337189","added_by":"auto","created_at":"2026-02-12 12:56:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79202,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot Distribution of Physicochemical and Microbiological Parameters of Water Samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/8bf91fd4167856109a0dc559.png"},{"id":102512662,"identity":"407614f9-9c2e-4dec-83aa-0b4654e200b7","added_by":"auto","created_at":"2026-02-12 12:56:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":317883,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of multiple physicochemical and microbiological parameters of groundwater in the Mocorito River aquifer. The base map is reproduced according to the Esri Terms of Use, which permit use in academic publications with attribution.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/d94604560c70aa8d47c0ad8c.png"},{"id":102512665,"identity":"f4d2bbd1-9199-45c8-945e-5293e5589e37","added_by":"auto","created_at":"2026-02-12 12:56:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":336019,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual variations of water quality parameters (2012–2022)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/1fafcd7418c170c940d36411.png"},{"id":102512737,"identity":"b4c616e8-c266-45fa-a529-e89d79b265d0","added_by":"auto","created_at":"2026-02-12 12:56:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":362678,"visible":true,"origin":"","legend":"\u003cp\u003eIonic ratio diagrams for the identification of hydrogeochemical processes in the aquifer (period 2012-2022): (a) Mg\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e vs Ca\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e, (b) HCO₃\u003csup\u003e2\u003c/sup\u003e⁻/Na\u003csup\u003e+\u003c/sup\u003e vs Ca\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e, (c) Ca\u003csup\u003e2+\u003c/sup\u003e/SO₄\u003csup\u003e2\u003c/sup\u003e⁻ vs Ca\u003csup\u003e2+\u003c/sup\u003e, (d) SO₄\u003csup\u003e2\u003c/sup\u003e⁻/Ca\u003csup\u003e2+\u003c/sup\u003e vs NO₃⁻/Ca\u003csup\u003e2+\u003c/sup\u003e, (e) NO₃⁻/Na\u003csup\u003e+\u003c/sup\u003e vs Cl⁻/Na\u003csup\u003e+\u003c/sup\u003e, (f) (SO₄\u003csup\u003e2\u003c/sup\u003e⁻ + HCO₃\u003csup\u003e2\u003c/sup\u003e⁻) vs (Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/2b7debb3fc74a49e0b38a909.png"},{"id":102512735,"identity":"374aba89-49e6-4790-a2cf-206f1802974f","added_by":"auto","created_at":"2026-02-12 12:56:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":297128,"visible":true,"origin":"","legend":"\u003cp\u003eGeochemical processes controlling groundwater chemistry in the study area: (a) Piper diagram showing hydrochemical facies and major ion composition; (b) distribution of MRA groundwater samples in the USSL diagram for irrigation suitability; (c) relationship between total dissolved solids (TDS) and total hardness (TH); and (d) relationship between NO₃⁻/Cl⁻ ratio and chloride concentration (Cl) indicating potential sources of nitrate contamination.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/e8caa97521e99cc91f54cff6.png"},{"id":102512759,"identity":"ed1b3bf9-4852-4516-b84b-5bf6077f80f5","added_by":"auto","created_at":"2026-02-12 12:57:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":488798,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrams of multivariate statistical analysis. (a) Correlogram of Pearson correlation results. (b) Loading plots of variables on the first two principal components (PCs). (c) Dendrogram showing three clusters from agglomerative hierarchical cluster analysis.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/3e9ceb2d0b46acf2c10a1160.png"},{"id":102512733,"identity":"b093b9ed-23cf-49a7-84e1-06a740cd4b14","added_by":"auto","created_at":"2026-02-12 12:56:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":265746,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the WQI in the state of Sinaloa, with a detailed view of the Mocorito River aquifer and scatter plot of WQI versus total dissolved solids (TDS) for the Mocorito River aquifer.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/edfe771c5db2c31fcc940900.png"},{"id":102746868,"identity":"34794d28-5708-43ff-a800-469affd65420","added_by":"auto","created_at":"2026-02-16 09:02:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3071669,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/3e00a6db-03d9-4a50-802a-05d22efe8d75.pdf"},{"id":102512657,"identity":"06563969-a0c3-46ae-9287-6cce3e0a4da7","added_by":"auto","created_at":"2026-02-12 12:56:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1584876,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALMRA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8629843/v1/56daf01c3b72b5f53e729ee5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Hydrogeochemical Processes Regulating Groundwater Quality in a Tropical Agricultural Landscape of Northwestern Mexico","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGroundwater is one of the main sources of water supply for human consumption, agricultural, and industrial activities worldwide (Khan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Its importance is accentuated in semiarid regions such northwestern Mexico (State of Sinaloa), where surface water sources are highly variable and subject to climatic stress and overexploitation (Bustos-Terrones et al. 2024). Groundwater quality depends on multiple factors, including natural hydrogeochemical processes, hydroclimatic conditions, land use changes, and anthropogenic activities that generate diffuse or point-source pollution (Wu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Particularly in densely populated urban areas, aquifer recharge zones are highly vulnerable to the infiltration of contaminants originating from landfills, untreated wastewater, and agricultural runoff, thereby compromising the water security of local communities (Krishnamoorthy \u0026amp; Lakshmanan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeasonal spatial analysis of groundwater quality has emerged as a critical tool for monitoring and integrated resource management, allowing the identification of spatiotemporal patterns, risk zones, and dominant processes that affect the chemical composition of water (Xie et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent studies, such as that of Pande et al (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in the Morna River Basin (India), have shown that groundwater quality varies significantly between pre- and post-monsoon seasons, influenced by processes such as water recharge, contaminant transport, and agricultural activity. Similarly, research conducted in industrial areas such as Ranipet (India) has detected seasonal fluctuations in the concentrations of heavy metals and dissolved salts, linked to anthropogenic sources and climatic dynamics (Wali et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings highlight the need to adopt a seasonal geospatial approach to more accurately assess groundwater vulnerability, particularly in contexts of high agricultural and industrial pressure such as northwestern Mexico (Bahukhandi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComplementarily, hydrogeochemistry has proven to be fundamental for unraveling the physical, chemical, and biogeochemical processes that determine groundwater quality (Rivera-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The analysis of parameters such as pH, electrical conductivity, total dissolved solids, and the concentration of major ions (Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, Na⁺, K⁺, Cl⁻, SO₄\u0026sup2;⁻, HCO₃⁻) allows for the characterization of water types and dominant processes, including mineral dissolution, weathering, ion exchange, and anthropogenic contamination (Salh et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hamma et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In agricultural regions, where fertilizers and pesticides are widely applied, hydrogeochemistry also enables the detection of processes such as salinization, saline intrusion, or nitrate leaching, which pose potential risks to human health and ecosystems (Saeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bahrami et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe integration of multivariate statistical and geochemical modeling tools, such as principal component analysis (PCA), and hydrogeochemical zoning through clustering, has enhanced the geospatial analysis of aquifers, allowing for the distinction between natural and anthropogenic sources of contamination (Xie et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother key component in the comprehensive analysis of groundwater quality is the application of Water Quality Index (WQI), which synthesize multiple physicochemical parameters into a single scale that allows water to be classified according to its suitability for human or agricultural use (Bahrami et al. 2023). International studies have demonstrated that these indices, combined with GIS and statistical analyses, are effective tools for assessing water resource vulnerability, identifying critical zones, and supporting decision-making in public policy (Xie et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, in agricultural and industrialized areas of India, Algeria, and China, WQIs have revealed that between 20% and 60% of the groundwater analyzed does not meet the standards for human consumption due to the presence of nitrates, fluoride, and heavy metals (Mohammed et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kacha et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies in southern Iran\u0026rsquo;s semi-arid regions have assessed groundwater quality for drinking, irrigation, and industrial uses. Bahrami et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlighted spatial variability in hydrochemical parameters, while Bahrami et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) applied WQI and GIS to identify areas at risk for potability. Bahrami and Zarei (2026) further demonstrated the utility of WQI combined with GIS and modeling as tools for integrated groundwater management.\u003c/p\u003e \u003cp\u003eOverall, the geospatial analysis of groundwater quality, supported by hydrogeochemical, geochemical, and socio-environmental approaches, has been consolidated as a comprehensive methodology to evaluate the sustainability of groundwater resources (Bustos-Terrones et al. 2024). In the case of northwestern Mexico (Sinaloa), where agricultural pressure, urban growth, and climate variability are critical factors, it is essential to implement methodologies that integrate the spatial, temporal, and social dimensions of water quality. This study addresses the following research questions: (1) How do groundwater quality parameters in the MRA vary spatially and seasonally? (2) To what extent do agricultural practices influence these variations? (3) Which hydrogeochemical factors most significantly affect aquifer vulnerability? By answering these questions using hydrochemical variables and GIS tools, the study aims to provide scientific evidence for informed decision-making and sustainable water resource management.\u003c/p\u003e"},{"header":"2. Data and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Analysis\u003c/h2\u003e \u003cp\u003eThis study included seven sampling sites, each sampled twice per year from 2012 to 2022. The aquifer covers over a broad area of the Sierra region, while the selected sites \u003cem\u003eare in\u003c/em\u003e zones affected by anthropogenic contamination from urban and agricultural activities (Rivera-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sampling points were strategically positioned in the lower basin to effectively capture the main sources of pollution.\u003c/p\u003e \u003cp\u003eThe data used in this study were obtained from the National Water Quality Monitoring Network (RENAMECA)(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gob.mx/conagua/es/articulos/resultados-de-la-red-nacional-de-medicion-de-calidad-del-agua-renameca\u003c/span\u003e\u003cspan address=\"https://www.gob.mx/conagua/es/articulos/resultados-de-la-red-nacional-de-medicion-de-calidad-del-agua-renameca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As all data are publicly accessible through RENAMECA, no special permits or additional authorizations were required for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.\u003cem\u003e2\u003c/em\u003e Groundwater Quality Assessment\u003c/h2\u003e \u003cp\u003eThe evaluation of the physicochemical quality of groundwater from the Mocorito River Aquifer was conducted through the collection and analysis of samples from seven wells within the study area. The seven sampling sites were strategically selected in areas with significant anthropogenic activities, located in low-elevation zones of the aquifer surface. Because the Mocorito River Aquifer is a coastal aquifer, the monitoring wells have variable depths ranging from 2 to 12 m. Site 1 is downstream of the Eustaquio Buelna Dam within an agricultural area. Site 2 is near agricultural fields and close to a small village, while Site 3 lies within a region of intensive farming. Site 4 is situated very close to a small town, representing a semi-urban environment. Site 5 is positioned between agricultural lands and near two small ranches, and Site 6 is found among intensive agricultural zones near a small ranch. Finally, Site 7 is in the center of the city of Angostura, representing an urban setting. Collectively, these sites provide a representative overview of the aquifer\u0026rsquo;s water quality across different land uses, allowing an assessment of both natural and anthropogenic influences.\u003c/p\u003e \u003cp\u003eGroundwater quality monitoring was carried out by collecting and analyzing water samples from wells chosen within the study region. The region has two well-defined climatic periods. The rainy season extends from June to October, while the remaining months correspond to the dry season, characterized by lower water availability and drier conditions. Sampling was performed during both the dry and rainy seasons to capture the temporal variation of the parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Temporal and Spatial Analysis\u003c/h2\u003e \u003cp\u003eFor the spatial analysis, data from seven sampling points located in the MRA were evaluated. Regarding the temporal analysis, the behavior of the water quality parameters was examined for the 2012\u0026ndash;2022 period, considering seasonal variations and potential changes associated with natural or anthropogenic factors. In this analysis, the results were illustrated using interpolated maps with color coding. The maps were created using open-source software (QGIS 3.14). Each map employs a color gradient, where green tones represent low concentrations and red tones indicate high concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Hydrogeochemical Analysis and Data Clustering\u003c/h2\u003e \u003cp\u003eTo identify the processes controlling the chemical composition of water, ionic ratio diagrams, TDS vs. TH, and NO₃⁻/Cl⁻ vs. Cl⁻ diagrams were constructed, as well as Piper diagrams, allowing differentiation of hydrochemical facies and evaluation of the influence of natural processes such as silicate and carbonate dissolution, weathering, and ion exchange, versus anthropogenic pressures associated with agriculture and wastewater. Irrigation suitability was assessed using the USSL diagram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.\u003c/b\u003e\u003cb\u003e5\u003c/b\u003e \u003cb\u003eWater Quality Index\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe WQI has been established as an effective tool for synthesizing complex water quality information into a single value reflecting its suitability for various uses (Basharat et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Water classification was performed according to Equations \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Tajwar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chaudhary et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The parameters considered include pH, total dissolved solids, total hardness, calcium, magnesium, nitrates, chlorides, sulfates, fluoride, and total alkalinity. Water quality was assessed by considering the relative importance of each parameter. The evaluated parameters included pH (8.5, weight 4), total dissolved solids (500 mg/L, weight 4), total hardness (300 mg/L, weight 3), calcium (75 mg/L, weight 3), magnesium (30 mg/L, weight 3), nitrates (45 mg/L, weight 4), chlorides (250 mg/L, weight 2), sulfates (200 mg/L, weight 2), fluorides (1 mg/L, weight 4), and total alkalinity (200 mg/L, weight 2), with their respective guideline limits established by the WHO and distinct weights assigned according to their influence on overall water quality.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{RW}_{i}=\\frac{{w}_{i}}{{\\sum\\:}_{i}^{n}{w}_{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{q}_{i}=\\frac{{e}_{i}-{v}_{i}}{{b}_{i}-{v}_{i}}\\text{*}100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{S.I.}_{i}={q}_{i\\:}\\text{*}{RW}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:WQI=\\sum\\:_{i=1}^{n}\\left({S.I.}_{i}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eWi\u003c/em\u003e indicates its significance and \u003cem\u003ewi\u003c/em\u003e represents the weight assigned to each variable. The score of each parameter (\u003cem\u003eqi\u003c/em\u003e) was calculated based on its measured concentration (\u003cem\u003eei\u003c/em\u003e) relative to the ideal value in pure water (\u003cem\u003evi\u003c/em\u003e), which is 0 for all parameters except pH, and the standard set by the WHO (\u003cem\u003ebi\u003c/em\u003e). The WQI values are grouped into five categories: excellent water (\u0026lt;\u0026thinsp;50), good water (50\u0026ndash;100), acceptable water (100\u0026ndash;200), poor water (200\u0026ndash;300), and unsuitable water (\u0026gt;\u0026thinsp;300) (Bustos-Terrones et al. 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Multivariate Statistical Analysis\u003c/h2\u003e \u003cp\u003eA multivariate statistical analysis was conducted to identify the processes controlling groundwater quality and to classify the sampling sites based on their hydrochemical similarity. Techniques such as Pearson correlation, principal component analysis, and hierarchical cluster analysis (HCA) were applied. Pearson correlation allowed the detection of linear associations between physicochemical and microbiological parameters, facilitating the identification of co-occurrence patterns and possible common contamination sources (Dheeraj et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). PCA reduced the dimensionality of the data and explained most of the variance through a limited number of components, highlighting dominant variables and groupings that reflect natural hydrochemical processes or anthropogenic influences (Kacha et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bahukhandi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, factor analysis with varimax rotation enabled the extraction of interpretable factors representing different contamination sources or specific hydrogeological conditions. Finally, HCA, using Ward\u0026rsquo;s method and Euclidean distance, grouped the sampling points according to physicochemical and microbiological similarities, delineating homogeneous zones, identifying spatial structures within the aquifer, and facilitating differentiated management of the groundwater resource (Alshahrani et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Abdullahi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Study Area","content":"\u003cp\u003eIn Mexico, there are 653 officially recognized aquifers. A considerable number of these exhibit some degree of overexploitation or lack of availability, which represents a significant challenge for the sustainable management of groundwater. The Mocorito River Aquifer is one of them, experiencing issues such as overexploitation, declining piezometric levels, and deterioration of water quality (Rivera-Hern\u0026aacute;ndez et al. 2017; Rivera-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The study area was selected due to its strategic importance for agricultural and mining development, as well as the interaction between human activities and water resources. The selection of the MRA is based on its regional economic importance and the challenges associated with sustainable groundwater management, including overextraction driven by agricultural demand, declining water quality linked to intensive fertilizer use and salinity accumulation, and increasing pressure from land-use change and prolonged drought conditions. The MRA exhibits topographic variation ranging from coastal plains to mountainous areas in the east, with the highest elevations, up to 565 m.a.s.l. located in the northeastern and southeastern extremes (Rivera-Hern\u0026aacute;ndez et al. 2017; Rivera-Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MRA, located northwest of Mexico, is a predominantly unconfined system composed of alluvial and fluvial sediments (including gravels, sands, silts, and clays) reaching thicknesses of over 200 m in the plain. The water table varies according to topography and recharge, generally ranging from 2 to 12 m below the surface, being shallower in the coastal plain and deeper toward the foothills. Groundwater flow is oriented approximately from northeast to southwest, parallel to the course of the Mocorito River. The aquifer is currently overexploited, with extraction exceeding recharge, and its proximity to the surface makes it relatively vulnerable to contamination. Nevertheless, its substantial thickness provides considerable groundwater storage, while its lithological composition influences geochemical processes that affect water quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe seven selected monitoring sites provide a representative overview of the aquifer\u0026rsquo;s water quality (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e presents the coordinates of the sampling points.). The aquifer extends from coastal to mountainous zones, with the central area characterized by rural settlements and intensive agriculture. The sites were strategically located in areas of highest anthropogenic impact, capturing the main factors affecting groundwater chemistry. As all sites belong to the same hydrogeological unit, the dataset reliably reflects the aquifer\u0026rsquo;s overall behavior, making the use of seven well-distributed sites methodologically sound.\u003c/p\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Water Quality Parameters\u003c/h2\u003e \u003cp\u003eThe results were compared with the WHO guidelines and recent research reports (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ammonia (0.0406 mg/L), nitrite (0.0133 mg/L), and nitrate (9.63 mg/L) were below the permissible limits, indicating low nitrogenous contamination and a moderate influence of agricultural practices or diffuse discharges, although nitrate is higher than reported in other studies (Saeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggesting a moderate influence of agricultural practices or diffuse discharges (Table S2 provides an overall statistical summary of the parameters, Table S3 shows ANOVA results comparing the seven sampling points, and Table S4 presents ANOVA results for the two seasonal periods, indicating that most parameters remained stable except for water temperature, which varied significantly between rainy and dry seasons).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Groundwater Quality Parameters with WHO Guidelines and Literature Data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulations*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThis study**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOther studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmmonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH₃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSaeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMohamed et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSaeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNO₃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e9.6364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePO₄\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.3044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSaeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal chlorides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e25.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal dissolved solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1007.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e449.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e876.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHydrogen potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003epH units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7.6063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMohamed et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal fluorides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.3344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSilica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSiO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e55.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eElectrical conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emS/cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNo guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1606.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e894.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal hardness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e488.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSaeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal alkalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e379.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e235.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSaeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSulfates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSO₄\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e165.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e75\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e126.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e46.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.2349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKhan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e181.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTaloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHCO₃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e379.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e307.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMohamed et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e28.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*WHO Guidelines (2004). ** Average\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe groundwater chemistry indicated high salinity and hardness. The total dissolved solids (1008.0 mg/L) and electrical conductivity (1606.4 \u0026micro;S/cm) exceeded recommended limits and were higher than values reported in other comparable studies (Khan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Taloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This mineralization was characterized by excessive total hardness (488.2 mg/L), suggesting a prevalence of calcium and magnesium salts, like the findings reported by Kashif et al (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Saeedi et al (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChlorides, sulfates, and total fluorides remained below the limits. The presence of silica (55.197 mg/L) and bicarbonates (379.758 mg/L) have no WHO guideline limits. Silica levels were higher than those reported in Zakariah et al (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which could be related to the weathering of silicate rocks. Total alkalinity (379.242 mg/L) indicated high buffering capacity, consistent with the findings of Mohamed et al (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Regarding cations, calcium (126.97 mg/L) and magnesium (46.25 mg/L) remained within the guidelines limits, although the values are high and contribute to water hardness. Sodium concentration (181.65 mg/L) was found close the guideline limit (200 mg/L), which could pose a risk for drinking water and irrigation. Potassium (4.23 mg/L) was within acceptable limits.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents boxplots showing that water quality tends toward high mineralization, evidenced by elevated total dissolved solids (TDS), electrical conductivity (EC), and total hardness (TH) (Al Haj et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In several cases, these parameters exceed WHO (2017) limits, reaching TDS concentrations up to 3000 mg/L and hardness values near 1500 mg/L. This behavior indicates a predominance of dissolved salts and calcium and magnesium carbonates, consistent with previous studies in agricultural areas where mineralization is the main water quality issue (Kashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Taloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough total alkalinity (TA) and bicarbonates (HCO₃⁻) enhance the buffering capacity of the water, they also contribute to hardness and reduce its suitability for human consumption and irrigation. Concerning nutrients, nitrate (NO₃⁻) and total nitrogen (TN) values remained within guidelines limits, with medians around 10\u0026ndash;15 mg/L, reflecting moderate agricultural influence. However, outliers reaching up to 30 mg/L were identified, corresponding to localized fertilization inputs and leaching processes (Khan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Taloor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Ammonium (NH₃) and total phosphorus (TP) were found at low concentrations, indicating minimal recent organic pollution and low eutrophication risk, although some phosphate outliers suggest possible fertilizer discharge events (Sodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBasic water quality parameters indicate chemically stable conditions, with pH values within the acceptable range (7.0\u0026ndash;8.2), suitable for aquatic life and human consumption after treatment. Silica (SiO₂) reaches relatively high values (40\u0026ndash;70 mg/L), associated with silicate rock weathering and posing no regulatory risk, although it contributes significantly to mineralization (Zakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Regarding chlorides (TCl) and sulfates (SO₄\u0026sup2;⁻), most records remain below guideline values, although peaks approaching 250 mg/L were observed, linked to anthropogenic discharges in specific areas (Khan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, these results suggest that the main limitation to water quality in the area is not nutrient enrichment but salinization, hardness, and sporadic fecal contamination, which is consistent with observations reported in other agricultural regions (Saeedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Spatial Analysis\u003c/h2\u003e \u003cp\u003eAll sampling points were selected at strategic locations. The seven points in the aquifer are situated in areas with the highest anthropogenic activity, and thus are considered representative for adequately describing the current state of the aquifer.\u003c/p\u003e \u003cp\u003eThe spatial analysis of water quality in the MRA revealed similar behavior among the seven sampling points (SP) during the period 2012\u0026ndash;2022, reflecting the interaction of natural processes and anthropogenic pressures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At SP1, located near the Eustaquio Buelna reservoir, the highest concentrations of TOC (2.227 mg/L), nutrients (NO₂⁻, ON, TN, TP), total hardness (1090.36 mg/L), and electrical conductivity (3625 \u0026micro;s/cm\u003csup\u003e2\u003c/sup\u003e) were observed, highlighting the influence of intensive fertilization practices and salt mobilization. At SP2, also agricultural but close to a human settlement, intermediate levels of nitrates (14.7 mg/L) and TDS (812.7 mg/L) were observed, reflecting the combined influence of agricultural practices and domestic discharges. SP3, located in an intensive farming area, exhibited high levels of fecal coliforms (211 MPN/100 mL), suggesting microbiological contamination linked to manure and wastewater use; the most critical microbial load was found at SP6, with 3474 MPN/100 mL of coliforms accompanied by phosphorus and ammonium, confirming severe water quality deterioration. Recent organic pollution at site SP5 was evidenced by high ammonium and fecal coliforms (138 MPN/100 mL), despite low nitrate concentrations (1.7 mg/L). This is consistent with the site's location in an area influenced by ranching and agriculture.\u003c/p\u003e \u003cp\u003eUrban points reflected different scenarios: salt and nutrient concentrations were lower than at agricultural sites, but coliforms (88.5 MPN/100 mL) were detected at SP4, which are associated with domestic discharges. The city of Angostura (SP7) showed the lowest nutrient and salt levels, but the presence of coliforms (25.5 MPN/100 mL) indicates a moderate microbiological impact.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese results confirm that agricultural sites (SP1, SP2, SP3, and SP6) concentrate the main problems of salinity, hardness, and fertilizer-derived nutrients, while urban sites (SP4 and SP7) present a higher sanitary risk due to microbiological contamination. These findings are consistent with the report by Rivera-Hern\u0026aacute;ndez et al (2017), who indicated weathering and evaporation as dominant processes in the aquifer\u0026rsquo;s chemical composition, but emphasized that groundwater is used for human consumption. According to these results, sustainable agricultural practices, control urban discharges, and continuous monitoring of the aquifer is required to ensure the quality and sustainability of the water resource in the region (Kashif et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, the analysis of microbiological parameters reveals that although most samples exhibit low fecal coliform (FC) concentrations, extreme values (24196 MPN/100mL, SP6, 2020) exceeding 10,000 MPN/100 mL indicate episodic contamination from domestic or livestock wastewater.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Seasonal Analysis\u003c/h2\u003e \u003cp\u003eThe concentrations of water parameters from the seven sampling points of the MRA during the 2012\u0026ndash;2022 period were analyzed. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the seasonal evaluation, which show marked fluctuations in water quality parameters, with peaks and declines in specific years. Overall, anthropogenic contaminants, such as nutrients and coliforms, tend to increase during the rainy seasons, associated with the runoff of fertilizers, organic matter, and wastewater into water bodies. In contrast, during the dry season, concentrations generally decrease due to reduced external inputs, although in some cases, evaporation concentrates dissolved salts, increasing parameters such as TDS, electrical conductivity (EC), and total hardness (TH). The nutrient dynamics (NH₃, NO₂⁻, NO₃⁻, TN, PO₄\u0026sup3;⁻, TP) exhibit seasonal variations consistent with agricultural leaching and surface runoff processes. For example, 2014 data show a peak in nitrates (NO₃⁻: 14.26 mg/L) and total phosphorus (TP: 0.285 mg/L), coinciding with years of higher precipitation (2013 and 2014) and reflecting the relationship between agricultural practices and eutrophication risk. During dry years, such as 2021 and 2022, although external inputs decrease, some salt concentration is observed (TDS between 408 and 456 mg/L; EC between 1413 and 1514 \u0026micro;S/cm), reflecting seasonal mineralization, a natural process in which reduced water volumes and evaporation temporarily increase dissolved salts and nutrient concentrations, affecting water quality and salinity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2021 and 2022, reduced water inputs led to a prolonged dry season. As a result, an decremento in salt concentration was observed, with TDS ranging from 408 to 456 mg/L and EC from 1413 to 1514 \u0026micro;S/cm, reflecting a seasonal mineralization effect. This indicates that water quality is modulated by the interaction between natural processes (evaporation, hydrological cycles, mineralization) and human activities (agriculture, domestic discharges).\u003c/p\u003e \u003cp\u003eThe interannual variability of other parameters, such as bicarbonate (HCO₃⁻: 339\u0026ndash;413 mg/L), calcium (Ca\u003csup\u003e2+\u003c/sup\u003e: 77\u0026ndash;178 mg/L), and magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e: 24\u0026ndash;59 mg/L), suggests a stable hydrochemical behavior, with slight increases during dry seasons (november to may). TOC and chlorides exhibited fluctuations associated with organic and urban inputs. Seasonal peaks of nutrients and coliforms during rainy (june to october) periods highlight the risk of water quality deterioration and the potential impact on public health and aquatic ecosystems. A similar spatial and temporal dynamic is observed in other semi-arid basins. Pande et al (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported significant variations between pre- and post-monsoon seasons, with marked differences in nutrients, salts, and coliforms according to land use. Krishnamoorthy and Lakshmanan (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated the influence of agricultural areas on fecal coliform and nutrient concentrations in India, while Wali et al (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported comparable distribution patterns of phosphorus, nitrates, and salts, dependent on seasonal and geographic factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Hydrogeochemical species\u003c/h2\u003e \u003cp\u003eThe hydrogeochemical analysis of the aquifer, based on ionic ratio diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicates that natural and anthropogenic processes interact to determine the chemical composition of groundwater. In the Mg\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e vs. Ca\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), the samples were in the silicate zone, suggesting that the chemistry is mainly controlled by the dissolution of silicate minerals (Al Haj et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The HCO₃\u003csup\u003e2\u003c/sup\u003e⁻/Na\u003csup\u003e+\u003c/sup\u003e vs. Ca\u003csup\u003e2+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) reflected a Ca\u003csup\u003e2+\u003c/sup\u003e contribution dominated by carbonates, evidencing the dissolution of carbonate minerals (Dong et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec (Ca\u003csup\u003e2+\u003c/sup\u003e/SO₄\u003csup\u003e2\u003c/sup\u003e⁻ vs. Ca\u003csup\u003e2+\u003c/sup\u003e) showed that calcium mainly originates from the dissolution of carbonates/dolomites, while the contribution of sulfates is minor (Pande et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Salh et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).The SO₄\u003csup\u003e2\u003c/sup\u003e⁻/Ca\u003csup\u003e2+\u003c/sup\u003e vs. NO₃⁻/Ca\u003csup\u003e2+\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) and NO₃⁻/Na\u003csup\u003e+\u003c/sup\u003e vs. Cl⁻/Na\u003csup\u003e+\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee) diagrams evidenced the influence of agricultural activities and domestic wastewater, with significant contributions of nitrates and chlorides, combined with the influence of natural minerals (Zakariah et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sakthi Priya et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, the SO₄\u003csup\u003e2\u003c/sup\u003e⁻ + HCO₃\u003csup\u003e2\u003c/sup\u003e⁻ vs. Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e diagram Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef indicated a balance between silicate weathering and calcite dissolution, reflecting an ionic balance close to electrochemical equilibrium and the stable interaction between natural mineral dissolution and silicate rock weathering processes (Teklearegay et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Marouf et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Miranda et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (See Table S5). These results indicate that the chemical composition of the aquifer is the result of the interaction between natural geochemical processes and anthropogenic pressures, with a relatively homogeneous pattern in the sampled sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Piper diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) shows distinct hydrochemical facies, with SP6\u0026ndash;SP7 dominated by Na\u0026sup2;⁺\u0026ndash;Cl⁻, SP2\u0026ndash;SP4 by Ca\u0026sup2;⁺\u0026ndash;Mg\u0026sup2;⁺\u0026ndash;HCO₃⁻, and SP1, SP3, and SP5 exhibiting mixed characteristics. These patterns reflect the combined influence of natural geochemical processes and anthropogenic pressures. Ionic balance results indicate stable conditions at Sites 1 and 6, higher variability at Sites 2 and 5, and consistently low but stable values at Sites 4 and 7, supporting data reliability and site-specific groundwater quality differences.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b) shows the diagram of the United States Salinity Laboratory Staff (USSL), which classifies water quality into 16 zones (C1 to C4 and S1 to S4) to assess its suitability for irrigation (Tajwar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Zones C1 and S1 indicate minimal risk, while C4 and S4 represent very high risk for irrigation. According to this figure, the USSL diagram classifies water quality as excellent (C1S1), good (C1S2, C2S1, and C2S2), poor (C1S3, C2S3, C3S1, C3S2, and C3S3), and very poor (C1S4, C2S4, C3S4, C4S1, C4S2, C4S3, and C4S4) for irrigation purposes (Mohallel et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Most of the water in the region (67%) shows quality suitable for irrigation, with acceptable salinity levels (EC) and low sodium adsorption risk (SAR), reflecting the overall good performance of the agricultural water resource. The SAR\u0026ndash;EC diagram indicates that the best water quality occurs in wells such as SP3 and SP5, where both salinity and sodium levels remain low, supporting safe irrigation use. Approximately 17% of the samples fall into the \u0026ldquo;Poor\u0026rdquo; category, suggesting potential limitations for salinity-sensitive crops and the need for specific agricultural management practices. The remaining 16% of the samples, corresponding to SP1, are classified as \u0026ldquo;Very Poor,\u0026rdquo; representing a high risk to soils due to elevated salinity and high exchangeable sodium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c) shows a clear distinction between soft, moderately hard, hard, and very hard waters, as well as between fresh and brackish waters. The distribution of sampling points in fields such as Hard-Fresh water and Hard-Brackish water reflects the influence of carbonate mineral dissolution, salt leaching, and the hydrogeochemical evolution of the aquifer. In the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(d), contamination sources are identified. High NO₃⁻ concentrations are mainly associated with local agricultural activities and human settlements. The increase in Cl⁻ indicates domestic infiltrations and salinization processes. The dispersion of the samples evidences the interaction of natural processes with anthropogenic pressures that enhance contaminant loads (Hossain et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar results were reported by Rivera-Hern\u0026aacute;ndez et al (2017) in the MRA, where weathering and evaporation were identified as dominant processes. These findings are related to cumulative impacts of nitrogen fertilization and discharges of excreta and wastewater (Sodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Multivariate Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe multivariate analysis was used to identify significant relationships among physicochemical parameters, nutrients, and microbiological variables, and to evidence the interaction between natural processes and anthropogenic pressures (Kashif Alam et al. 2025; Sodomon et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Pearson correlation showed strong associations among NO₃⁻, NO₂⁻, TN, TOC, TDS, EC, and cations such as Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, Na⁺, as well as HCO₃⁻, suggesting that water quality is simultaneously influenced by agricultural and domestic inputs and natural processes such as carbonate and silicate dissolution (Fig.\u0026nbsp;7a) (Table S6). These findings are consistent with previous studies in agricultural aquifers, where PCA and correlation analyses effectively distinguished the contributions of natural and anthropogenic sources (Chai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrincipal Component Analysis revealed that two components explained most of the water quality variability (49.1%) in the aquifer. Parameters such as TDS, EC, TH, SO₄\u0026sup2;⁻, Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, and Mn\u0026sup2;⁺ were associated with mineral dissolution processes, silicate and carbonate weathering, as well as inputs from fertilizers and domestic discharges (Fig.\u0026nbsp;7b) (Table S7). Other components highlighted the variability of nutrients and organic compounds (PO₄\u0026sup3;⁻, TP, TOC, MBAS, FC), reflecting diffuse and domestic pollution. These patterns are consistent with findings in arid and semi-arid regions, where PCA enabled the identification of hydrochemical facies and predominant processes controlling groundwater quality (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003ec\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHierarchical cluster analysis allowed grouping hydrogeochemical variables according to their similarity, identifying three main clusters (Fig.\u0026nbsp;7c). Cluster 1 included NO₃⁻, TN, TA, HCO₃⁻, Na⁺, Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, SO₄\u0026sup2;⁻, EC, TDS, TH, and Mn\u0026sup2;⁺, primarily associated with natural hydrogeochemical processes and mineralization. Cluster 2 grouped ON, MBAS, RP, and TCl, representing variables related to moderate anthropogenic inputs, while Cluster 3 included WT, FC, NH₃, TP, PO₄\u0026sup3;⁻, NO₂⁻, TOC, K⁺, pH, TF, and SiO₂, reflecting recent contamination and active chemical\u0026ndash;biological processes. These results demonstrate that groundwater quality in the MRA is governed by the interaction of geogenic processes, water\u0026ndash;rock interactions, and human contributions. These results emphasize the importance of multivariate methods for identifying critical parameters and planning sustainable monitoring and management strategies (Fallatah et al. 2023; Hagage et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Laghrib et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Water Quality Index\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the spatial distribution of the WQI in the Northwest Mexico, with a focus on the MRA. In Sinaloa, the WQI ranges from 10 (best quality, in blue) to 200 (worst quality, in red). Green points correspond to water sampling stations or sites. Spatial interpolation shows that most sampling sites exhibit low water quality index values (blue and green tones), indicating good water quality, with only isolated areas showing higher values. A WQI gradient is observed, ranging from green to red, with a central core in red and yellow tones indicating the highest index values, associated with poorer water quality. Sampling points are distributed across both low and high index areas, allowing for the spatial identification of critical zones. The scatter plot between the WQI and Total Dissolved Solids (TDS) for the aquifers of Sinaloa shows a clear positive correlation: as the concentration of dissolved solids increases, the water quality index deteriorates. This reflects both natural salinization processes and the influence of intensive agricultural activities and wastewater discharges (Abiye et al. 2025; Pandey et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Contributions of this work to global climate change mitigation\u003c/h2\u003e \u003cp\u003eThis study contributes to climate change mitigation through the sustainable management of water resources and pollution control. By enhancing the understanding of groundwater quality, it enables the design of strategies that reduce aquifer overexploitation and increase the resilience of ecosystems and communities under adverse climatic scenarios. The identification of pollution sources allows for the implementation of targeted measures that indirectly decrease greenhouse gas emissions by reducing energy-intensive water treatments and promoting sustainable agricultural practices. Furthermore, it highlights the importance of sustainable water and waste management policies, fostering the reduction of environmental degradation and improving carbon sequestration, thereby strengthening water security and contributing to global climate change mitigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe groundwater quality of the MRA is characterized by low contamination from nitrogenous and phosphorous nutrients, but exhibits high mineralization, hardness, and dissolved salts concentration, which limits its suitability for human consumption and irrigation without treatment. Spatial and seasonal variability indicates that agricultural sites concentrate the highest salinity and nutrient issues, while urban areas show moderate microbiological risks. Hydrogeochemical analyses suggest that the aquifer\u0026rsquo;s composition results from the interaction between natural processes, such as silicate and carbonate dissolution, and anthropogenic pressures, including agricultural fertilization and domestic wastewater discharges. Multivariate analyses, Piper diagram, allowed the identification of contamination patterns, dominant hydrochemical facies, and critical parameters, highlighting the usefulness of these tools for sustainable resource management. Finally, the observed inverse relationship between water availability and the Water Quality Index indicates that overexploitation and intensive agricultural practices contribute to water quality degradation, emphasizing the need to implement differentiated management strategies that integrate recharge, salinity control, and reduction of pollutant inputs to ensure the aquifer\u0026rsquo;s sustainability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture research\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFuture research should focus on continuous monitoring of the MRA to capture temporal variations in water quality, particularly during rainy (june to october) and dry seasons (november to may), when nutrient leaching, salinization, and microbial contamination are more pronounced. Long-term datasets would allow for better understanding of seasonal dynamics and the impact of agricultural practices on groundwater. Hydrogeochemical modeling and spatial-temporal analysis are recommended to predict the evolution of water quality under different land use and climate change scenarios. Research should also explore mitigation measures to reduce salinity, hardness, and nutrient loads, including sustainable irrigation practices, artificial recharge, and targeted treatments for microbial contamination. These strategies can support both environmental protection and public health. Finally, integrating hydrochemical, land use, and socio-economic data can inform adaptive management policies, enabling sustainable groundwater use and preservation in tropical agricultural regions. Such interdisciplinary approaches are essential to ensure long-term water quality and availability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYABT: Project administration, Investigation, Writing \u0026ndash; original draft. OMA: Investigation, Formal analysis. JGL: Investigation, Writing \u0026ndash;review \u0026amp; editing, Validation, Software. JGRP: Writing \u0026ndash;review \u0026amp; editing. BRP: Visualization. JEM: Validation, Data curation. TAK: Writing \u0026ndash;review \u0026amp; editing. MNRV: Validation.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe lead author gratefully acknowledges the support provided by SECIHTI through the Programa de Investigadoras e Investigadores por M\u0026eacute;xico (Project No. 7026).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eNo datasets were generated or analyzed during the current study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullahi IM, Abubakar L, Saidu G, Yusuf H (2025) Hydrogeochemical characterization of groundwater using water quality index and multivariate statistical analysis in Binji town and environs, Sokoto Basin, northwestern Nigeria. Appl Water Sci 15(2):33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13201-025-02358-9\u003c/span\u003e\u003cspan address=\"10.1007/s13201-025-02358-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbiye T, Raimi MO (2025) Assessing groundwater contamination near dumpsites in Port Harcourt using water quality index (WQI): insights from seasonal and distance-based variations. Int J Hydrology 9(1):35\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15406/ijh.2025.09.00401\u003c/span\u003e\u003cspan address=\"10.15406/ijh.2025.09.00401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Haj R, Merheb M, Halwani J, Ouddane B (2025) Baseline hydrogeochemical characteristics of groundwater in Abu Ali watershed (northern Lebanon). J Hydrology: Reg Stud 57:102135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrh.2024.102135\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrh.2024.102135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlshahrani M, Ahmad M, Laiq M, Nabi M (2025) Geostatistical analysis and multivariate assessment of groundwater quality. Sci Rep 15(1):7435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-91055-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-91055-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahrami A, Bahrami M, Haghani E (2024) Groundwater quality assessment for potable use using WQI and GIS technology in southern Iran. Sustainable Water Resour Manage 10(5):177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40899-024-01155-7\u003c/span\u003e\u003cspan address=\"10.1007/s40899-024-01155-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahrami M, Khaksar E, Bahrami A (2022) Groundwater quality evaluation for potable and irrigation uses in the semi-arid region of southern Iran. Irrig Sci 71(3):749\u0026ndash;765. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ird.2671\u003c/span\u003e\u003cspan address=\"10.1002/ird.2671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahrami M, Zarei AR (2023) Assessment and modeling of groundwater quality for drinking, irrigation, and industrial purposes using water quality indices and GIS technique in Fasarud aquifer (Iran). Model Earth Syst Environ 9(4):3907\u0026ndash;3921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-023-01725-2\u003c/span\u003e\u003cspan address=\"10.1007/s40808-023-01725-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahukhandi KD, Sk B, Kamboj V, Bhan U, Goswami L, Kushwaha A et al (2025) Assessment of spring water hydrogeochemistry in the intermountain Doon Valley of the Himalayan region using water quality indexing and multivariate statistical methods. Water Air Soil Pollut 236(5):123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11270-025-07892-5\u003c/span\u003e\u003cspan address=\"10.1007/s11270-025-07892-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasharat H, Ahmed T, Ahmad SS, Zahir M, Scholz M (2025) Integrating water quality index and advanced geographic information system for groundwater quantity and quality mapping: insights from Islamabad\u0026rsquo;s aquifer. Sustainability 17(4):1373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su17041373\u003c/span\u003e\u003cspan address=\"10.3390/su17041373\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBustos Terrones YA, Loaiza JG, Rojas-Valencia MN, Rangel-Peraza JG, Ram\u0026iacute;rez-Pereda B, Garc\u0026iacute;a-S\u0026aacute;nchez BE (2024) Hydrogeochemical characterization of groundwater located in an intensive agricultural area: the Culiacan River Aquifer case study. Water Resour 51(5):844\u0026ndash;859. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1134/S0097807824603212\u003c/span\u003e\u003cspan address=\"10.1134/S0097807824603212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChai Y, Xiao C, Li M, Liang X (2020) Hydrogeochemical characteristics and groundwater quality evaluation based on multivariate statistical analysis. Water 12(10):2792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w12102792\u003c/span\u003e\u003cspan address=\"10.3390/w12102792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaudhary R, Gaur N, Yadav M (2024) Hydrogeochemical analysis of groundwater quality during the pre-monsoon season of Manipur, India. Water Sci 38(1):274\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23570008.2024.2341369\u003c/span\u003e\u003cspan address=\"10.1080/23570008.2024.2341369\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDheeraj VP, Singh CS, Alam A, Sonkar AK (2025) Hydrogeochemical quality investigation of groundwater resources using multivariate statistical methods, water quality indices, and health risk assessment in the Korba Coalfield region, India. Stoch Env Res Risk Assess 39:122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00477-024-02895-w\u003c/span\u003e\u003cspan address=\"10.1007/s00477-024-02895-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong F, Yin H, Yang Z, Zhou W, Cheng W, Liu Y (2025) Delineating the controlling mechanisms of geothermal water quality and suitability zoning in the Lower Yellow River Basin, China. Environ Technol Innov 38:104126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eti.2025.104126\u003c/span\u003e\u003cspan address=\"10.1016/j.eti.2025.104126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbri E, Bassey NE, George NJ, Harry TA (2025) Geochemical analysis of groundwater in Central Cross River State: implications for water quality and public health. Researchers J Sci Technol 5(2):16\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFallatah O, Khattab MR (2023) Study of hydrogeochemical factors affecting groundwater quality used for land reclamation: application of multivariate statistical analysis. Stoch Env Res Risk Assess 37(12):4719\u0026ndash;4735. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00477-023-02537-7\u003c/span\u003e\u003cspan address=\"10.1007/s00477-023-02537-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagage M, Hewaidy AGA, Abdulaziz AM (2025) Groundwater quality assessment for drinking, irrigation, aquaculture, and industrial uses in the waterlogged northeastern Nile Delta, Egypt: a multivariate statistical approach and water quality indices. Model Earth Syst Environ 11(1):59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-024-02242-6\u003c/span\u003e\u003cspan address=\"10.1007/s40808-024-02242-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamma B, Alodah A, Bouaicha F, Bekkouche MF, Barkat A, Hussein EE (2024) Hydrochemical assessment of groundwater using multivariate statistical methods and water quality indices (WQIs). Appl Water Sci 14(2):33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13201-023-02084-0\u003c/span\u003e\u003cspan address=\"10.1007/s13201-023-02084-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao Q, Xiao Y, Liu K, Yang H, Chen H, Wang L et al (2025) Spatial pattern of groundwater chemistry in a typical piedmont plain of Northern China driven by natural and anthropogenic forces. Sci Rep 15(1):7643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-91659-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-91659-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MS, Nahar N, Shaibur MR, Bhuiyan MT, Siddique AB, Al Maruf A et al (2024) Hydrochemical characteristics and groundwater quality evaluation in south western region of Bangladesh: A GIS-based approach and multivariate analyses. Heliyon 10(1):e24011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e24011\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e24011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKacha N, Aouidane L, Boulabeiz M, Khammar H, Tellil B (2025) Integrated hydrochemical assessment of groundwater quality in El Mahmel Plain, Algeria: A hydrochemical, water quality index and multivariate statistical approach. Water Air Soil Pollut 236(7):121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11270-025-08050-7\u003c/span\u003e\u003cspan address=\"10.1007/s11270-025-08050-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashif A, Muhammad N, Wajid A, Said M, Abdur R (2025) Geogenic contamination of groundwater in a highland watershed: Hydrogeochemical assessment, source apportionment, and health risk evaluation of fluoride and nitrate. Hydrology 12(4):70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/hydrology12040070\u003c/span\u003e\u003cspan address=\"10.3390/hydrology12040070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan MYA, ElKashouty M, Abdellattif A, Egbueri JC, Taha AI, Al Deep M et al (2023) Influence of natural and anthropogenic factors on the hydrogeology and hydrogeochemistry of Wadi Itwad Aquifer, Saudi Arabia: Assessment using multivariate statistics and PMWIN simulation. Ecol Ind 151:110287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2023.110287\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2023.110287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnamoorthy L, Lakshmanan VR (2025) Seasonal assessment of groundwater quality, hydrogeochemistry, and heavy metal pollution in groundwater at Ranipet District: employing multivariate statistics, agricultural indices, and health risk evaluation. EGU General Assembly 2025, EGU25-1041. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/egusphere-egu25-1041\u003c/span\u003e\u003cspan address=\"10.5194/egusphere-egu25-1041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaghrib F, Elkasmi S, Bahaj T, Barbot A, Bouzekraoui M, Hilali M et al (2025) Integration of multivariate statistical analysis, geochemical modeling, and irrigation water quality assessment in the aquifers of the South Atlas Tinghir\u0026ndash;Errachidia\u0026ndash;Boudenib Basin (Pre-African Trough, Morocco). J Afr Earth Sc 221:105444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jafrearsci.2024.105444\u003c/span\u003e\u003cspan address=\"10.1016/j.jafrearsci.2024.105444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Hu X, Zhu H, Xing L, Han Z, Hu K et al (2025a) Analysis of the hydrogeochemical characteristics of groundwater and identification of pollution sources in facility agriculture areas using self-organizing neural networks. Environ Earth Sci 84(6):161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-025-12114-6\u003c/span\u003e\u003cspan address=\"10.1007/s12665-025-12114-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu N, Chen M, Gao D, Wu Y, Wang X (2025b) Identification of hydrogeochemical processes in shallow groundwater using multivariate statistical analysis and inverse geochemical modeling. Environ Monit Assess 197(2):135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-024-13528-8\u003c/span\u003e\u003cspan address=\"10.1007/s10661-024-13528-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Zhou L, Ma X, Li W, Li J (2025c) Comprehensive study of groundwater hydrochemistry, driving forces, and health risks in representative rural agglomerations, Northern China. ACS Omega 10(18):18391\u0026ndash;18403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsomega.4c10697\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.4c10697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarouf AA, Ameen HA, Qasim MJ (2025) Water quality index utilization for groundwater quality assessment for wells in Zakho District, Kurdistan Region, Iraq. Water Sci 39(1):325\u0026ndash;335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23570008.2025.2496580\u003c/span\u003e\u003cspan address=\"10.1080/23570008.2025.2496580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiranda J, Antunes M, Ribeiro CA (2025) Groundwater modeling from urban areas (NW Portugal): An integrated hydrological\u0026ndash;hydrogeological approach. Earth Syst Environ 9:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s41748-025-00614-1\u003c/span\u003e\u003cspan address=\"10.1007/s41748-025-00614-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohallel SA, Morgan H, Elgendy A, Maharjan S, Fazli S, Li W et al (2025) Innovative machine learning, isotopic, and hydrogeochemical techniques for groundwater analysis in arid landscapes in Egypt\u0026rsquo;s Eastern Desert. Earth Syst Environ 9:1\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s41748-025-00628-9\u003c/span\u003e\u003cspan address=\"10.1007/s41748-025-00628-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohamed AK, Liu D, Song K, Mohamed MA, Aldaw E, Elubid BA (2019) Hydrochemical analysis and fuzzy logic method for evaluation of groundwater quality in the North Chengdu Plain, China. Int J Environ Res Public Health 16(3):302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph16030302\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16030302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammed MA, Szab\u0026oacute; NP, Mikita V, Szűcs P (2025) Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of the Debrecen area, Hungary: Integration of classical and unsupervised learning methods. Environ Sci Pollut Res 32(11):6884\u0026ndash;6903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-025-36175-z\u003c/span\u003e\u003cspan address=\"10.1007/s11356-025-36175-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePande CB, Tolche AD, Egbueri JC, Mohd Sidek L, Singh R, Mishra AP et al (2025) Implications of seasonal variations of hydrogeochemical analysis using GIS, WQI, and statistical analysis method for the semi-arid region. Appl Water Sci 15(4):80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13201-025-02387-4\u003c/span\u003e\u003cspan address=\"10.1007/s13201-025-02387-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey HK, Singh VK, Srivastava SK, Singh RP (2023) Groundwater quality assessment using PCA and water quality index (WQI) in a drought-prone area. Sustainable Water Resour Manage 9(6):197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40899-023-00963-7\u003c/span\u003e\u003cspan address=\"10.1007/s40899-023-00963-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRivera-Hern\u0026aacute;ndez JR, Green-Ruiz CR, Pelling-Salazar LE, Flegal AR (2021) Monitoring of As, Cd, Cr, and Pb in groundwater of Mexico\u0026rsquo;s agriculture Mocorito River Aquifer: Implications for risks to human health. Water Air Soil Pollut 232(7):291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11270-021-05238-5\u003c/span\u003e\u003cspan address=\"10.1007/s11270-021-05238-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRivera Hern\u0026aacute;ndez JR, Green Ruiz C, Pelling Salazar L, Trejo Alduenda A (2017) Hidroqu\u0026iacute;mica del acu\u0026iacute;fero costero del R\u0026iacute;o Mocorito, Sinaloa, M\u0026eacute;xico: Evaluaci\u0026oacute;n de la calidad del agua para consumo humano y agricultura. Hidrobiol\u0026oacute;gica 27(1):103\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24275/uam/izt/dcbi/hidro/2017v27n1/Green\u003c/span\u003e\u003cspan address=\"10.24275/uam/izt/dcbi/hidro/2017v27n1/Green\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeedi R, Sadeghi S, Massoudinejad M, Oroskhan M, Mohagheghian A, Mohebbi M et al (2024) Assessing drinking water quality based on water quality indices, human health risk, and burden of disease attributable to heavy metals in rural communities of Yazd County, Iran (2015\u0026ndash;2021). Heliyon 10(13):e33984. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e33984\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e33984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakthi Priya R, Antony Ravindran A, Richard Abishek S (2025) Spatial assessment of submarine groundwater discharge influence on aquifer water quality in the coastal region of Chettikulam to Kolachel, southern India: Using SMI and HFED techniques. Environ Geochem Health 47(4):112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10653-025-02379-y\u003c/span\u003e\u003cspan address=\"10.1007/s10653-025-02379-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalh YHM, Su C, Iqbal J, Usman US, Yousif MH, Ismail O (2025) Hydrogeochemical processes regulating groundwater quality and its suitability for drinking purposes in the recent alluvial plain. Blue Nile Region Sudan Environ Geochem Health 47(4):1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10653-025-02409-9\u003c/span\u003e\u003cspan address=\"10.1007/s10653-025-02409-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarker MMR, Hermans T, Van Camp M, Hossain D, Islam M, Ahmed N et al (2022) Identifying the major hydrogeochemical factors governing groundwater chemistry in the coastal aquifers of Southwest Bangladesh using statistical analysis. Hydrology 9(2):20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/hydrology9020020\u003c/span\u003e\u003cspan address=\"10.3390/hydrology9020020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingha SS, Singha S, Kumar P (2025) Groundwater quality assessment in Nalgonda District, Telangana, India: A comprehensive approach using self-organizing map. Discover Sustain 6(1):185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s43621-025-00985-5\u003c/span\u003e\u003cspan address=\"10.1007/s43621-025-00985-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSodomon AK, Akpataku KV, Tampo L, Mande SLAS, Herrera JB, Rosales WM et al (2025) Assessment of hydrogeochemical evolution of groundwater from the basement aquifer in the upper part of the transboundary Mono River Basin, Togo. J Hydrology: Reg Stud 58:102200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrh.2025.102200\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrh.2025.102200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTajwar M, Rahman M, Hasan M, Sakib N, Shreya SS, Alam MMT et al (2025) Interpreting hydrogeochemical interactions and controlling processes in groundwater using advanced statistical techniques in the Southeast Asian megacity of Dhaka, Bangladesh. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clwat.2025.100084\u003c/span\u003e\u003cspan address=\"10.1016/j.clwat.2025.100084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cleaner Water 100084\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTajwar M, Uddin A, Lee MK, Nelson J, Zahid A, Sakib N (2023) Hydrochemical characterization and quality assessment of groundwater in Hatiya Island, southeastern coastal region of Bangladesh. Water 15(5):905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w15050905\u003c/span\u003e\u003cspan address=\"10.3390/w15050905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaloor AK, Sambyal S, Sharma R, Dev S, Shastri S, Kumar R (2025) Advanced hydrogeochemical facies classification: A comparative analysis of machine learning models with SMOTE in the Tawi Basin. Phys Chem Earth Parts A/B/C 137:103785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pce.2024.103785\u003c/span\u003e\u003cspan address=\"10.1016/j.pce.2024.103785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeklearegay T, Atlabachew A, Abebe A, Jothimani M (2025) Comprehensive hydrogeochemical and statistical assessment of groundwater quality for drinking and irrigation in the Demie River catchment, Southern Ethiopia. Discover Appl Sci 7(5):1\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42452-025-06967-6\u003c/span\u003e\u003cspan address=\"10.1007/s42452-025-06967-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWali SU, Alias NB, Usman AA, Umar A, Muhammad N, Kaoje IU et al (2025) Geostatistical and multivariate analysis of phosphate evolution and its relationship with heavy metals in shallow groundwater in a semi-arid basin. Earth Sci Inf 18(3):267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12145-025-01771-7\u003c/span\u003e\u003cspan address=\"10.1007/s12145-025-01771-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu C, Zhou H, Lu C, Zhao Y, Liu R, Zhan L et al (2025) Groundwater nitrate responses to extreme rainfall in alluvial\u0026ndash;diluvial plain aquifers: Evidence from hydrogeochemistry and isotopes. J Contam Hydrol 104584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jconhyd.2025.104584\u003c/span\u003e\u003cspan address=\"10.1016/j.jconhyd.2025.104584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Z, Liu W, Chen S, Yao R, Yang C, Zhang X et al (2025) Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environments in a metropolis. J Hydrology: Reg Stud 58:102227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrh.2025.102227\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrh.2025.102227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZakariah MNA, Roslan N, Sulaiman N, Aznan MA, Al Farishi B (2025) Hydrogeochemical assessment of groundwater quality of Muda River Basin, Kedah, Malaysia. Malaysian J Fundamental Appl Sci 21(1):1513\u0026ndash;1528. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11113/mjfas.v21n1.3368\u003c/span\u003e\u003cspan address=\"10.11113/mjfas.v21n1.3368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Water quality parameters, Sustainable management, Environmental indicators, Groundwater quality, Hydrochemistry, Agricultural impact","lastPublishedDoi":"10.21203/rs.3.rs-8629843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8629843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assesses the hydrochemical and water quality characteristics of the Mocorito River Aquifer (MRA), located in a tropical region with strong agricultural influence. Water data from seven sites of the aquifer, collected during 2012\u0026ndash;2022, were used and evaluated through hydrogeochemical features, water quality index (WQI), and multivariate analysis to identify spatial and seasonal patterns, and anthropogenic effects. The results found low total nitrogen (9.7084 mg/L) but high mineralization and water hardness. High levels of sodium (up to 630.4 mg/L) and fecal coliforms (up to 24196 NMP/100mL) make the water unsuitable for both drinking and irrigation. Spatial and seasonal analyses showed heterogeneity among sites, with the greatest water quality deterioration in agricultural areas resulting from intensive fertilization and the leaching of soluble salts and fertilizer-derived ions (NO₃⁻, Cl⁻, SO₄\u0026sup2;⁻, HCO₃⁻, Na⁺, Ca\u0026sup2;⁺, Mg\u0026sup2;⁺) into the aquifer due to excessive irrigation, and in urban areas from microbiological contamination. Hydrogeochemical assessment indicated that aquifer composition results from the interaction of natural processes (silicate and carbonate dissolution, ion exchange). Piper and USSL diagrams were used to characterize hydrochemical facies and evaluate irrigation suitability, while multivariate analysis demonstrated that groundwater quality in the MRA is controlled by the combined effects of geogenic processes, water\u0026ndash;rock interactions, and anthropogenic influences. The MRA aquifer generally maintains good-to-moderate water quality, but localized zones show severe deterioration due to salinity and agricultural pollution, indicating the need for continuous monitoring and sustainable management.\u003c/p\u003e","manuscriptTitle":"Analysis of Hydrogeochemical Processes Regulating Groundwater Quality in a Tropical Agricultural Landscape of Northwestern Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 12:55:54","doi":"10.21203/rs.3.rs-8629843/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-12T10:57:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T06:10:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230504450209822430308082289010545888241","date":"2026-02-11T22:44:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236643064637501134844798411225909159057","date":"2026-02-09T17:49:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256878992048945660670171241509394097096","date":"2026-02-09T16:18:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T15:31:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T13:50:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T13:49:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2026-01-18T07:33:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0e8f7562-259b-4688-8dc1-177f252a9e8f","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-12T10:57:43+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T11:21:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 12:55:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8629843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8629843","identity":"rs-8629843","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}